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Update links in compiled bindings.

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This commit is contained in:
Ryan Curtin
2024-08-28 17:06:13 -04:00
parent b164d69848
commit 567e3482b3
5 changed files with 89 additions and 84 deletions
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doc/user/bindings/cli.md
+18 -17
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@@ -216,7 +216,7 @@ $ mlpack_bayesian_linear_regression --input_model_file blr_model.bin
- [Bayesian Interpolation](https://cs.uwaterloo.ca/~mannr/cs886-w10/mackay-bayesian.pdf)
- [Bayesian Linear Regression, Section 3.3](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
- [BayesianLinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp)
- [BayesianLinearRegression C++ class documentation](../../user/methods/bayesian_linear_regression.md)
## mlpack_cf
{: #cf }
@@ -1210,7 +1210,7 @@ $ mlpack_hoeffding_tree --input_model_file tree.bin --test_file test_set.arff
- [mlpack_decision_tree](#decision_tree)
- [mlpack_random_forest](#random_forest)
- [Mining High-Speed Data Streams (pdf)](http://dm.cs.washington.edu/papers/vfdt-kdd00.pdf)
- [HoeffdingTree class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp)
- [HoeffdingTree class documentation](../../user/methods/hoeffding_tree.md)
## mlpack_kde
{: #kde }
@@ -1575,7 +1575,7 @@ $ mlpack_lars --input_model_file lasso_model.bin --test_file test.csv
- [mlpack_linear_regression](#linear_regression)
- [Least angle regression (pdf)](https://mlpack.org/papers/lars.pdf)
- [LARS C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lars/lars.hpp)
- [LARS C++ class documentation](../../user/methods/lars.md)
## mlpack_linear_svm
{: #linear_svm }
@@ -1665,7 +1665,7 @@ $ mlpack_linear_svm --input_model_file lsvm_model.bin --test_file test.csv
- [mlpack_random_forest](#random_forest)
- [mlpack_logistic_regression](#logistic_regression)
- [LinearSVM on Wikipedia](https://en.wikipedia.org/wiki/Support-vector_machine)
- [LinearSVM C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_svm/linear_svm.hpp)
- [LinearSVM C++ class documentation](../../user/methods/linear_svm.md)
## mlpack_lmnn
{: #lmnn }
@@ -1767,7 +1767,7 @@ $ mlpack_lmnn --input_file letter_recognition.csv --k 5 --update_interval 10
- [mlpack_nca](#nca)
- [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
- [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
- [LMNN C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lmnn/lmnn.hpp)
- [LMNN C++ class documentation](../../user/methods/lmnn.md)
## mlpack_local_coordinate_coding
{: #local_coordinate_coding }
@@ -1943,7 +1943,7 @@ $ mlpack_logistic_regression --input_model_file lr_model.bin --test_file
- [mlpack_softmax_regression](#softmax_regression)
- [mlpack_random_forest](#random_forest)
- [Logistic regression on Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)
- [:LogisticRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/logistic_regression/logistic_regression.hpp)
- [:LogisticRegression C++ class documentation](../../user/methods/logistic_regression.md)
## mlpack_lsh
{: #lsh }
@@ -2086,7 +2086,7 @@ $ mlpack_mean_shift --input_file data.csv --centroid_file centroids.csv
- [mlpack_dbscan](#dbscan)
- [Mean shift on Wikipedia](https://en.wikipedia.org/wiki/Mean_shift)
- [Mean Shift, Mode Seeking, and Clustering (pdf)](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1c168275c59ba382588350ee1443537f59978183)
- [mlpack::mean_shift::MeanShift C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/mean_shift/mean_shift.hpp)
- [mlpack::mean_shift::MeanShift C++ class documentation](../../user/methods/mean_shift.md)
## mlpack_nbc
{: #nbc }
@@ -2165,7 +2165,7 @@ $ mlpack_nbc --input_model_file nbc_model.bin --test_file test_set.csv
- [mlpack_softmax_regression](#softmax_regression)
- [mlpack_random_forest](#random_forest)
- [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
- [NaiveBayesClassifier C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp)
- [NaiveBayesClassifier C++ class documentation](../../user/methods/naive_bayes_classifier.md)
## mlpack_nca
{: #nca }
@@ -2239,7 +2239,7 @@ By default, the SGD optimizer is used.
- [mlpack_lmnn](#lmnn)
- [Neighbourhood components analysis on Wikipedia](https://en.wikipedia.org/wiki/Neighbourhood_components_analysis)
- [Neighbourhood components analysis (pdf)](http://papers.nips.cc/paper/2566-neighbourhood-components-analysis.pdf)
- [NCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/nca/nca.hpp)
- [NCA C++ class documentation](../../user/methods/nca.md)
## mlpack_knn
{: #knn }
@@ -2465,7 +2465,8 @@ $ mlpack_nmf --input_file V.csv --w_file W.csv --h_file H.csv --rank 10
- [mlpack_cf](#cf)
- [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
- [Algorithms for non-negative matrix factorization (pdf)](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf)
- [AMF C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/amf/amf.hpp)
- [NMF C++ class documentation](../../user/methods/nmf.md)
- [AMF C++ class documentation](../../user/methods/amf.md)
## mlpack_pca
{: #pca }
@@ -2525,7 +2526,7 @@ $ mlpack_pca --input_file data.csv --new_dimensionality 5
### See also
- [Principal component analysis on Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)
- [PCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/pca/pca.hpp)
- [PCA C++ class documentation](../../user/methods/pca.md)
## mlpack_perceptron
{: #perceptron }
@@ -2597,7 +2598,7 @@ Note that all of the options may be specified at once: predictions may be calcul
- [mlpack_adaboost](#adaboost)
- [Perceptron on Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
- [Perceptron C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/perceptron/perceptron.hpp)
- [Perceptron C++ class documentation](../../user/methods/perceptron.md)
## mlpack_preprocess_split
{: #preprocess_split }
@@ -3014,7 +3015,7 @@ $ mlpack_radical --input_file X.csv --replicates 40 --output_ic_file ic.csv
- [Independent component analysis on Wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis)
- [ICA using spacings estimates of entropy (pdf)](https://www.jmlr.org/papers/volume4/learned-miller03a/learned-miller03a.pdf)
- [Radical C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/radical/radical.hpp)
- [Radical C++ class documentation](../../user/methods/radical.md)
## mlpack_random_forest
{: #random_forest }
@@ -3103,7 +3104,7 @@ $ mlpack_random_forest --input_model_file rf_model.bin --test_file
- [mlpack_softmax_regression](#softmax_regression)
- [Random forest on Wikipedia](https://en.wikipedia.org/wiki/Random_forest)
- [Random forests (pdf)](https://www.eecis.udel.edu/~shatkay/Course/papers/BreimanRandomForests2001.pdf)
- [RandomForest C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/random_forest/random_forest.hpp)
- [RandomForest C++ class documentation](../../user/methods/random_forest.md)
## mlpack_krann
{: #krann }
@@ -3261,7 +3262,7 @@ $ mlpack_softmax_regression --input_model_file sr_model.bin --test_file
- [mlpack_logistic_regression](#logistic_regression)
- [mlpack_random_forest](#random_forest)
- [Multinomial logistic regression (softmax regression) on Wikipedia](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)
- [SoftmaxRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/softmax_regression/softmax_regression.hpp)
- [SoftmaxRegression C++ class documentation](../../user/methods/softmax_regression.md)
## mlpack_sparse_coding
{: #sparse_coding }
@@ -3348,7 +3349,7 @@ $ mlpack_sparse_coding --input_model_file model.bin --test_file otherdata.csv
- [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
- [Efficient sparse coding algorithms (pdf)](http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf)
- [Regularization and variable selection via the elastic net](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=46217f372a75dddc2254fdbc6b9418ba3554e453)
- [SparseCoding C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/sparse_coding/sparse_coding.hpp)
- [SparseCoding C++ class documentation](../../user/methods/sparse_coding.md)
## mlpack_adaboost
{: #adaboost }
@@ -3503,7 +3504,7 @@ $ mlpack_linear_regression --input_model_file lr_model.bin --test_file
- [mlpack_lars](#lars)
- [Linear regression on Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)
- [LinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_regression/linear_regression.hpp)
- [LinearRegression C++ class documentation](../../user/methods/linear_regression.md)
## mlpack_preprocess_imputer
{: #preprocess_imputer }
doc/user/bindings/go.md
+18 -17
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@@ -263,7 +263,7 @@ _, test_predictions, stds := mlpack.BayesianLinearRegression(param)
- [Bayesian Interpolation](https://cs.uwaterloo.ca/~mannr/cs886-w10/mackay-bayesian.pdf)
- [Bayesian Linear Regression, Section 3.3](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
- [BayesianLinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp)
- [BayesianLinearRegression C++ class documentation](../../user/methods/bayesian_linear_regression.md)
## Cf()
{: #cf }
@@ -1467,7 +1467,7 @@ _, predictions, class_probs := mlpack.HoeffdingTree(param)
- [DecisionTree()](#decision_tree)
- [RandomForest()](#random_forest)
- [Mining High-Speed Data Streams (pdf)](http://dm.cs.washington.edu/papers/vfdt-kdd00.pdf)
- [HoeffdingTree class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp)
- [HoeffdingTree class documentation](../../user/methods/hoeffding_tree.md)
## Kde()
{: #kde }
@@ -1923,7 +1923,7 @@ _, test_predictions := mlpack.Lars(param)
- [LinearRegression()](#linear_regression)
- [Least angle regression (pdf)](https://mlpack.org/papers/lars.pdf)
- [LARS C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lars/lars.hpp)
- [LARS C++ class documentation](../../user/methods/lars.md)
## LinearSvm()
{: #linear_svm }
@@ -2042,7 +2042,7 @@ _, predictions, _ := mlpack.LinearSvm(param)
- [RandomForest()](#random_forest)
- [LogisticRegression()](#logistic_regression)
- [LinearSVM on Wikipedia](https://en.wikipedia.org/wiki/Support-vector_machine)
- [LinearSVM C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_svm/linear_svm.hpp)
- [LinearSVM C++ class documentation](../../user/methods/linear_svm.md)
## Lmnn()
{: #lmnn }
@@ -2173,7 +2173,7 @@ _, output, _ := mlpack.Lmnn(letter_recognition, param)
- [Nca()](#nca)
- [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
- [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
- [LMNN C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lmnn/lmnn.hpp)
- [LMNN C++ class documentation](../../user/methods/lmnn.md)
## LocalCoordinateCoding()
{: #local_coordinate_coding }
@@ -2396,7 +2396,7 @@ _, predictions, _ := mlpack.LogisticRegression(param)
- [SoftmaxRegression()](#softmax_regression)
- [RandomForest()](#random_forest)
- [Logistic regression on Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)
- [:LogisticRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/logistic_regression/logistic_regression.hpp)
- [:LogisticRegression C++ class documentation](../../user/methods/logistic_regression.md)
## Lsh()
{: #lsh }
@@ -2570,7 +2570,7 @@ centroids, _ := mlpack.MeanShift(data, param)
- [Dbscan()](#dbscan)
- [Mean shift on Wikipedia](https://en.wikipedia.org/wiki/Mean_shift)
- [Mean Shift, Mode Seeking, and Clustering (pdf)](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1c168275c59ba382588350ee1443537f59978183)
- [mlpack::mean_shift::MeanShift C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/mean_shift/mean_shift.hpp)
- [mlpack::mean_shift::MeanShift C++ class documentation](../../user/methods/mean_shift.md)
## Nbc()
{: #nbc }
@@ -2666,7 +2666,7 @@ _, predictions, _ := mlpack.Nbc(param)
- [SoftmaxRegression()](#softmax_regression)
- [RandomForest()](#random_forest)
- [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
- [NaiveBayesClassifier C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp)
- [NaiveBayesClassifier C++ class documentation](../../user/methods/naive_bayes_classifier.md)
## Nca()
{: #nca }
@@ -2758,7 +2758,7 @@ By default, the SGD optimizer is used.
- [Lmnn()](#lmnn)
- [Neighbourhood components analysis on Wikipedia](https://en.wikipedia.org/wiki/Neighbourhood_components_analysis)
- [Neighbourhood components analysis (pdf)](http://papers.nips.cc/paper/2566-neighbourhood-components-analysis.pdf)
- [NCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/nca/nca.hpp)
- [NCA C++ class documentation](../../user/methods/nca.md)
## Knn()
{: #knn }
@@ -3036,7 +3036,8 @@ H, W := mlpack.Nmf(V, 10, param)
- [Cf()](#cf)
- [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
- [Algorithms for non-negative matrix factorization (pdf)](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf)
- [AMF C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/amf/amf.hpp)
- [NMF C++ class documentation](../../user/methods/nmf.md)
- [AMF C++ class documentation](../../user/methods/amf.md)
## Pca()
{: #pca }
@@ -3110,7 +3111,7 @@ data_mod := mlpack.Pca(data, param)
### See also
- [Principal component analysis on Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)
- [PCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/pca/pca.hpp)
- [PCA C++ class documentation](../../user/methods/pca.md)
## Perceptron()
{: #perceptron }
@@ -3200,7 +3201,7 @@ Note that all of the options may be specified at once: predictions may be calcul
- [Adaboost()](#adaboost)
- [Perceptron on Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
- [Perceptron C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/perceptron/perceptron.hpp)
- [Perceptron C++ class documentation](../../user/methods/perceptron.md)
## PreprocessSplit()
{: #preprocess_split }
@@ -3728,7 +3729,7 @@ ic, _ := mlpack.Radical(X, param)
- [Independent component analysis on Wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis)
- [ICA using spacings estimates of entropy (pdf)](https://www.jmlr.org/papers/volume4/learned-miller03a/learned-miller03a.pdf)
- [Radical C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/radical/radical.hpp)
- [Radical C++ class documentation](../../user/methods/radical.md)
## RandomForest()
{: #random_forest }
@@ -3842,7 +3843,7 @@ _, predictions, _ := mlpack.RandomForest(param)
- [SoftmaxRegression()](#softmax_regression)
- [Random forest on Wikipedia](https://en.wikipedia.org/wiki/Random_forest)
- [Random forests (pdf)](https://www.eecis.udel.edu/~shatkay/Course/papers/BreimanRandomForests2001.pdf)
- [RandomForest C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/random_forest/random_forest.hpp)
- [RandomForest C++ class documentation](../../user/methods/random_forest.md)
## Krann()
{: #krann }
@@ -4042,7 +4043,7 @@ _, predictions, _ := mlpack.SoftmaxRegression(param)
- [LogisticRegression()](#logistic_regression)
- [RandomForest()](#random_forest)
- [Multinomial logistic regression (softmax regression) on Wikipedia](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)
- [SoftmaxRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/softmax_regression/softmax_regression.hpp)
- [SoftmaxRegression C++ class documentation](../../user/methods/softmax_regression.md)
## SparseCoding()
{: #sparse_coding }
@@ -4152,7 +4153,7 @@ codes, _, _ := mlpack.SparseCoding(param)
- [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
- [Efficient sparse coding algorithms (pdf)](http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf)
- [Regularization and variable selection via the elastic net](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=46217f372a75dddc2254fdbc6b9418ba3554e453)
- [SparseCoding C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/sparse_coding/sparse_coding.hpp)
- [SparseCoding C++ class documentation](../../user/methods/sparse_coding.md)
## Adaboost()
{: #adaboost }
@@ -4344,7 +4345,7 @@ _, X_test_responses := mlpack.LinearRegression(param)
- [Lars()](#lars)
- [Linear regression on Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)
- [LinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_regression/linear_regression.hpp)
- [LinearRegression C++ class documentation](../../user/methods/linear_regression.md)
## ImageConverter()
{: #image_converter }
doc/user/bindings/julia.md
+18 -17
View File
@@ -221,7 +221,7 @@ julia> _, test_predictions, stds =
- [Bayesian Interpolation](https://cs.uwaterloo.ca/~mannr/cs886-w10/mackay-bayesian.pdf)
- [Bayesian Linear Regression, Section 3.3](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
- [BayesianLinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp)
- [BayesianLinearRegression C++ class documentation](../../user/methods/bayesian_linear_regression.md)
## cf()
{: #cf }
@@ -1215,7 +1215,7 @@ julia> _, predictions, class_probs =
- [decision_tree()](#decision_tree)
- [random_forest()](#random_forest)
- [Mining High-Speed Data Streams (pdf)](http://dm.cs.washington.edu/papers/vfdt-kdd00.pdf)
- [HoeffdingTree class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp)
- [HoeffdingTree class documentation](../../user/methods/hoeffding_tree.md)
## kde()
{: #kde }
@@ -1590,7 +1590,7 @@ julia> _, test_predictions = lars(input_model=lasso_model,
- [linear_regression()](#linear_regression)
- [Least angle regression (pdf)](https://mlpack.org/papers/lars.pdf)
- [LARS C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lars/lars.hpp)
- [LARS C++ class documentation](../../user/methods/lars.md)
## linear_svm()
{: #linear_svm }
@@ -1683,7 +1683,7 @@ julia> _, predictions, _ = linear_svm(input_model=lsvm_model,
- [random_forest()](#random_forest)
- [logistic_regression()](#logistic_regression)
- [LinearSVM on Wikipedia](https://en.wikipedia.org/wiki/Support-vector_machine)
- [LinearSVM C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_svm/linear_svm.hpp)
- [LinearSVM C++ class documentation](../../user/methods/linear_svm.md)
## lmnn()
{: #lmnn }
@@ -1788,7 +1788,7 @@ julia> _, output, _ = lmnn(letter_recognition; k=5,
- [nca()](#nca)
- [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
- [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
- [LMNN C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lmnn/lmnn.hpp)
- [LMNN C++ class documentation](../../user/methods/lmnn.md)
## local_coordinate_coding()
{: #local_coordinate_coding }
@@ -1967,7 +1967,7 @@ julia> _, predictions, _ = logistic_regression(input_model=lr_model,
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Logistic regression on Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)
- [:LogisticRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/logistic_regression/logistic_regression.hpp)
- [:LogisticRegression C++ class documentation](../../user/methods/logistic_regression.md)
## lsh()
{: #lsh }
@@ -2107,7 +2107,7 @@ julia> centroids, _ = mean_shift(data)
- [dbscan()](#dbscan)
- [Mean shift on Wikipedia](https://en.wikipedia.org/wiki/Mean_shift)
- [Mean Shift, Mode Seeking, and Clustering (pdf)](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1c168275c59ba382588350ee1443537f59978183)
- [mlpack::mean_shift::MeanShift C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/mean_shift/mean_shift.hpp)
- [mlpack::mean_shift::MeanShift C++ class documentation](../../user/methods/mean_shift.md)
## nbc()
{: #nbc }
@@ -2187,7 +2187,7 @@ julia> _, predictions, _ = nbc(input_model=nbc_model,
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
- [NaiveBayesClassifier C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp)
- [NaiveBayesClassifier C++ class documentation](../../user/methods/naive_bayes_classifier.md)
## nca()
{: #nca }
@@ -2260,7 +2260,7 @@ By default, the SGD optimizer is used.
- [lmnn()](#lmnn)
- [Neighbourhood components analysis on Wikipedia](https://en.wikipedia.org/wiki/Neighbourhood_components_analysis)
- [Neighbourhood components analysis (pdf)](http://papers.nips.cc/paper/2566-neighbourhood-components-analysis.pdf)
- [NCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/nca/nca.hpp)
- [NCA C++ class documentation](../../user/methods/nca.md)
## knn()
{: #knn }
@@ -2482,7 +2482,8 @@ julia> H, W = nmf(V, 10; update_rules="multdist")
- [cf()](#cf)
- [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
- [Algorithms for non-negative matrix factorization (pdf)](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf)
- [AMF C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/amf/amf.hpp)
- [NMF C++ class documentation](../../user/methods/nmf.md)
- [AMF C++ class documentation](../../user/methods/amf.md)
## pca()
{: #pca }
@@ -2543,7 +2544,7 @@ julia> data_mod = pca(data; decomposition_method="randomized",
### See also
- [Principal component analysis on Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)
- [PCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/pca/pca.hpp)
- [PCA C++ class documentation](../../user/methods/pca.md)
## perceptron()
{: #perceptron }
@@ -2618,7 +2619,7 @@ Note that all of the options may be specified at once: predictions may be calcul
- [adaboost()](#adaboost)
- [Perceptron on Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
- [Perceptron C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/perceptron/perceptron.hpp)
- [Perceptron C++ class documentation](../../user/methods/perceptron.md)
## preprocess_split()
{: #preprocess_split }
@@ -3045,7 +3046,7 @@ julia> ic, _ = radical(X; replicates=40)
- [Independent component analysis on Wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis)
- [ICA using spacings estimates of entropy (pdf)](https://www.jmlr.org/papers/volume4/learned-miller03a/learned-miller03a.pdf)
- [Radical C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/radical/radical.hpp)
- [Radical C++ class documentation](../../user/methods/radical.md)
## random_forest()
{: #random_forest }
@@ -3137,7 +3138,7 @@ julia> _, predictions, _ = random_forest(input_model=rf_model,
- [softmax_regression()](#softmax_regression)
- [Random forest on Wikipedia](https://en.wikipedia.org/wiki/Random_forest)
- [Random forests (pdf)](https://www.eecis.udel.edu/~shatkay/Course/papers/BreimanRandomForests2001.pdf)
- [RandomForest C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/random_forest/random_forest.hpp)
- [RandomForest C++ class documentation](../../user/methods/random_forest.md)
## krann()
{: #krann }
@@ -3297,7 +3298,7 @@ julia> _, predictions, _ = softmax_regression(input_model=sr_model,
- [logistic_regression()](#logistic_regression)
- [random_forest()](#random_forest)
- [Multinomial logistic regression (softmax regression) on Wikipedia](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)
- [SoftmaxRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/softmax_regression/softmax_regression.hpp)
- [SoftmaxRegression C++ class documentation](../../user/methods/softmax_regression.md)
## sparse_coding()
{: #sparse_coding }
@@ -3385,7 +3386,7 @@ julia> codes, _, _ = sparse_coding(input_model=model,
- [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
- [Efficient sparse coding algorithms (pdf)](http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf)
- [Regularization and variable selection via the elastic net](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=46217f372a75dddc2254fdbc6b9418ba3554e453)
- [SparseCoding C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/sparse_coding/sparse_coding.hpp)
- [SparseCoding C++ class documentation](../../user/methods/sparse_coding.md)
## adaboost()
{: #adaboost }
@@ -3545,7 +3546,7 @@ julia> _, X_test_responses = linear_regression(input_model=lr_model,
- [lars()](#lars)
- [Linear regression on Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)
- [LinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_regression/linear_regression.hpp)
- [LinearRegression C++ class documentation](../../user/methods/linear_regression.md)
## image_converter()
{: #image_converter }
doc/user/bindings/python.md
+17 -16
View File
@@ -222,7 +222,7 @@ Because the estimator computes a predictive distribution instead of a simple poi
- [Bayesian Interpolation](https://cs.uwaterloo.ca/~mannr/cs886-w10/mackay-bayesian.pdf)
- [Bayesian Linear Regression, Section 3.3](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
- [BayesianLinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp)
- [BayesianLinearRegression C++ class documentation](../../user/methods/bayesian_linear_regression.md)
## cf()
{: #cf }
@@ -1235,7 +1235,7 @@ Then, this tree may be used to make predictions on the test set `'test_set'`, sa
- [decision_tree()](#decision_tree)
- [random_forest()](#random_forest)
- [Mining High-Speed Data Streams (pdf)](http://dm.cs.washington.edu/papers/vfdt-kdd00.pdf)
- [HoeffdingTree class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp)
- [HoeffdingTree class documentation](../../user/methods/hoeffding_tree.md)
## kde()
{: #kde }
@@ -1607,7 +1607,7 @@ The following command uses the `'lasso_model'` to provide predicted responses fo
- [linear_regression()](#linear_regression)
- [Least angle regression (pdf)](https://mlpack.org/papers/lars.pdf)
- [LARS C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lars/lars.hpp)
- [LARS C++ class documentation](../../user/methods/lars.md)
## linear_svm()
{: #linear_svm }
@@ -1701,7 +1701,7 @@ Then, to use that model to predict classes for the dataset '`'test'`', storing t
- [random_forest()](#random_forest)
- [logistic_regression()](#logistic_regression)
- [LinearSVM on Wikipedia](https://en.wikipedia.org/wiki/Support-vector_machine)
- [LinearSVM C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_svm/linear_svm.hpp)
- [LinearSVM C++ class documentation](../../user/methods/linear_svm.md)
## lmnn()
{: #lmnn }
@@ -1806,7 +1806,7 @@ Another program call making use of update interval & regularization parameter wi
- [nca()](#nca)
- [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
- [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
- [LMNN C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lmnn/lmnn.hpp)
- [LMNN C++ class documentation](../../user/methods/lmnn.md)
## local_coordinate_coding()
{: #local_coordinate_coding }
@@ -1988,7 +1988,7 @@ Then, to use that model to predict classes for the dataset '`'test'`', storing t
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Logistic regression on Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)
- [:LogisticRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/logistic_regression/logistic_regression.hpp)
- [:LogisticRegression C++ class documentation](../../user/methods/logistic_regression.md)
## lsh()
{: #lsh }
@@ -2135,7 +2135,7 @@ For example, to run mean shift clustering on the dataset `'data'` and store the
- [dbscan()](#dbscan)
- [Mean shift on Wikipedia](https://en.wikipedia.org/wiki/Mean_shift)
- [Mean Shift, Mode Seeking, and Clustering (pdf)](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1c168275c59ba382588350ee1443537f59978183)
- [mlpack::mean_shift::MeanShift C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/mean_shift/mean_shift.hpp)
- [mlpack::mean_shift::MeanShift C++ class documentation](../../user/methods/mean_shift.md)
## nbc()
{: #nbc }
@@ -2216,7 +2216,7 @@ Then, to use `'nbc_model'` to predict the classes of the dataset `'test_set'` an
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
- [NaiveBayesClassifier C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp)
- [NaiveBayesClassifier C++ class documentation](../../user/methods/naive_bayes_classifier.md)
## nca()
{: #nca }
@@ -2291,7 +2291,7 @@ By default, the SGD optimizer is used.
- [lmnn()](#lmnn)
- [Neighbourhood components analysis on Wikipedia](https://en.wikipedia.org/wiki/Neighbourhood_components_analysis)
- [Neighbourhood components analysis (pdf)](http://papers.nips.cc/paper/2566-neighbourhood-components-analysis.pdf)
- [NCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/nca/nca.hpp)
- [NCA C++ class documentation](../../user/methods/nca.md)
## knn()
{: #knn }
@@ -2524,7 +2524,8 @@ For example, to run NMF on the input matrix `'V'` using the 'multdist' update ru
- [cf()](#cf)
- [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
- [Algorithms for non-negative matrix factorization (pdf)](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf)
- [AMF C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/amf/amf.hpp)
- [NMF C++ class documentation](../../user/methods/nmf.md)
- [AMF C++ class documentation](../../user/methods/amf.md)
## pca()
{: #pca }
@@ -2586,7 +2587,7 @@ For example, to reduce the dimensionality of the matrix `'data'` to 5 dimensions
### See also
- [Principal component analysis on Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)
- [PCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/pca/pca.hpp)
- [PCA C++ class documentation](../../user/methods/pca.md)
## perceptron()
{: #perceptron }
@@ -2660,7 +2661,7 @@ Note that all of the options may be specified at once: predictions may be calcul
- [adaboost()](#adaboost)
- [Perceptron on Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
- [Perceptron C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/perceptron/perceptron.hpp)
- [Perceptron C++ class documentation](../../user/methods/perceptron.md)
## preprocess_split()
{: #preprocess_split }
@@ -3092,7 +3093,7 @@ For example, to perform ICA on the matrix `'X'` with 40 replicates, saving the i
- [Independent component analysis on Wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis)
- [ICA using spacings estimates of entropy (pdf)](https://www.jmlr.org/papers/volume4/learned-miller03a/learned-miller03a.pdf)
- [Radical C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/radical/radical.hpp)
- [Radical C++ class documentation](../../user/methods/radical.md)
## random_forest()
{: #random_forest }
@@ -3183,7 +3184,7 @@ Then, to use that model to classify points in `'test_set'` and print the test er
- [softmax_regression()](#softmax_regression)
- [Random forest on Wikipedia](https://en.wikipedia.org/wiki/Random_forest)
- [Random forests (pdf)](https://www.eecis.udel.edu/~shatkay/Course/papers/BreimanRandomForests2001.pdf)
- [RandomForest C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/random_forest/random_forest.hpp)
- [RandomForest C++ class documentation](../../user/methods/random_forest.md)
## krann()
{: #krann }
@@ -3347,7 +3348,7 @@ Then, to use `'sr_model'` to classify the test points in `'test_points'`, saving
- [logistic_regression()](#logistic_regression)
- [random_forest()](#random_forest)
- [Multinomial logistic regression (softmax regression) on Wikipedia](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)
- [SoftmaxRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/softmax_regression/softmax_regression.hpp)
- [SoftmaxRegression C++ class documentation](../../user/methods/softmax_regression.md)
## sparse_coding()
{: #sparse_coding }
@@ -3436,7 +3437,7 @@ Then, this model could be used to encode a new matrix, `'otherdata'`, and save t
- [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
- [Efficient sparse coding algorithms (pdf)](http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf)
- [Regularization and variable selection via the elastic net](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=46217f372a75dddc2254fdbc6b9418ba3554e453)
- [SparseCoding C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/sparse_coding/sparse_coding.hpp)
- [SparseCoding C++ class documentation](../../user/methods/sparse_coding.md)
## class Adaboost
{: #adaboost }
doc/user/bindings/r.md
+18 -17
View File
@@ -220,7 +220,7 @@ R> stds <- output$stds
- [Bayesian Interpolation](https://cs.uwaterloo.ca/~mannr/cs886-w10/mackay-bayesian.pdf)
- [Bayesian Linear Regression, Section 3.3](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
- [BayesianLinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp)
- [BayesianLinearRegression C++ class documentation](../../user/methods/bayesian_linear_regression.md)
## cf()
{: #cf }
@@ -1216,7 +1216,7 @@ R> class_probs <- output$probabilities
- [decision_tree()](#decision_tree)
- [random_forest()](#random_forest)
- [Mining High-Speed Data Streams (pdf)](http://dm.cs.washington.edu/papers/vfdt-kdd00.pdf)
- [HoeffdingTree class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp)
- [HoeffdingTree class documentation](../../user/methods/hoeffding_tree.md)
## kde()
{: #kde }
@@ -1583,7 +1583,7 @@ R> test_predictions <- output$output_predictions
- [linear_regression()](#linear_regression)
- [Least angle regression (pdf)](https://mlpack.org/papers/lars.pdf)
- [LARS C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lars/lars.hpp)
- [LARS C++ class documentation](../../user/methods/lars.md)
## linear_svm()
{: #linear_svm }
@@ -1676,7 +1676,7 @@ R> predictions <- output$predictions
- [random_forest()](#random_forest)
- [logistic_regression()](#logistic_regression)
- [LinearSVM on Wikipedia](https://en.wikipedia.org/wiki/Support-vector_machine)
- [LinearSVM C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_svm/linear_svm.hpp)
- [LinearSVM C++ class documentation](../../user/methods/linear_svm.md)
## lmnn()
{: #lmnn }
@@ -1780,7 +1780,7 @@ R> output <- output$output
- [nca()](#nca)
- [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
- [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
- [LMNN C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lmnn/lmnn.hpp)
- [LMNN C++ class documentation](../../user/methods/lmnn.md)
## local_coordinate_coding()
{: #local_coordinate_coding }
@@ -1960,7 +1960,7 @@ R> predictions <- output$predictions
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Logistic regression on Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)
- [:LogisticRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/logistic_regression/logistic_regression.hpp)
- [:LogisticRegression C++ class documentation](../../user/methods/logistic_regression.md)
## lsh()
{: #lsh }
@@ -2105,7 +2105,7 @@ R> centroids <- output$centroid
- [dbscan()](#dbscan)
- [Mean shift on Wikipedia](https://en.wikipedia.org/wiki/Mean_shift)
- [Mean Shift, Mode Seeking, and Clustering (pdf)](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1c168275c59ba382588350ee1443537f59978183)
- [mlpack::mean_shift::MeanShift C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/mean_shift/mean_shift.hpp)
- [mlpack::mean_shift::MeanShift C++ class documentation](../../user/methods/mean_shift.md)
## nbc()
{: #nbc }
@@ -2185,7 +2185,7 @@ R> predictions <- output$predictions
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
- [NaiveBayesClassifier C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp)
- [NaiveBayesClassifier C++ class documentation](../../user/methods/naive_bayes_classifier.md)
## nca()
{: #nca }
@@ -2259,7 +2259,7 @@ By default, the SGD optimizer is used.
- [lmnn()](#lmnn)
- [Neighbourhood components analysis on Wikipedia](https://en.wikipedia.org/wiki/Neighbourhood_components_analysis)
- [Neighbourhood components analysis (pdf)](http://papers.nips.cc/paper/2566-neighbourhood-components-analysis.pdf)
- [NCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/nca/nca.hpp)
- [NCA C++ class documentation](../../user/methods/nca.md)
## knn()
{: #knn }
@@ -2490,7 +2490,8 @@ R> H <- output$h
- [cf()](#cf)
- [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
- [Algorithms for non-negative matrix factorization (pdf)](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf)
- [AMF C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/amf/amf.hpp)
- [NMF C++ class documentation](../../user/methods/nmf.md)
- [AMF C++ class documentation](../../user/methods/amf.md)
## pca()
{: #pca }
@@ -2551,7 +2552,7 @@ R> data_mod <- output$output
### See also
- [Principal component analysis on Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)
- [PCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/pca/pca.hpp)
- [PCA C++ class documentation](../../user/methods/pca.md)
## perceptron()
{: #perceptron }
@@ -2624,7 +2625,7 @@ Note that all of the options may be specified at once: predictions may be calcul
- [adaboost()](#adaboost)
- [Perceptron on Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
- [Perceptron C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/perceptron/perceptron.hpp)
- [Perceptron C++ class documentation](../../user/methods/perceptron.md)
## preprocess_split()
{: #preprocess_split }
@@ -3048,7 +3049,7 @@ R> ic <- output$output_ic
- [Independent component analysis on Wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis)
- [ICA using spacings estimates of entropy (pdf)](https://www.jmlr.org/papers/volume4/learned-miller03a/learned-miller03a.pdf)
- [Radical C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/radical/radical.hpp)
- [Radical C++ class documentation](../../user/methods/radical.md)
## random_forest()
{: #random_forest }
@@ -3138,7 +3139,7 @@ R> predictions <- output$predictions
- [softmax_regression()](#softmax_regression)
- [Random forest on Wikipedia](https://en.wikipedia.org/wiki/Random_forest)
- [Random forests (pdf)](https://www.eecis.udel.edu/~shatkay/Course/papers/BreimanRandomForests2001.pdf)
- [RandomForest C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/random_forest/random_forest.hpp)
- [RandomForest C++ class documentation](../../user/methods/random_forest.md)
## krann()
{: #krann }
@@ -3299,7 +3300,7 @@ R> predictions <- output$predictions
- [logistic_regression()](#logistic_regression)
- [random_forest()](#random_forest)
- [Multinomial logistic regression (softmax regression) on Wikipedia](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)
- [SoftmaxRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/softmax_regression/softmax_regression.hpp)
- [SoftmaxRegression C++ class documentation](../../user/methods/softmax_regression.md)
## sparse_coding()
{: #sparse_coding }
@@ -3386,7 +3387,7 @@ R> codes <- output$codes
- [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
- [Efficient sparse coding algorithms (pdf)](http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf)
- [Regularization and variable selection via the elastic net](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=46217f372a75dddc2254fdbc6b9418ba3554e453)
- [SparseCoding C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/sparse_coding/sparse_coding.hpp)
- [SparseCoding C++ class documentation](../../user/methods/sparse_coding.md)
## adaboost()
{: #adaboost }
@@ -3544,7 +3545,7 @@ R> X_test_responses <- output$output_predictions
- [lars()](#lars)
- [Linear regression on Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)
- [LinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_regression/linear_regression.hpp)
- [LinearRegression C++ class documentation](../../user/methods/linear_regression.md)
## image_converter()
{: #image_converter }