diff --git a/src/mlpack/methods/linear_svm/linear_svm_main.cpp b/src/mlpack/methods/linear_svm/linear_svm_main.cpp
index 19864e6cb8..a095b57874 100644
--- a/src/mlpack/methods/linear_svm/linear_svm_main.cpp
+++ b/src/mlpack/methods/linear_svm/linear_svm_main.cpp
@@ -108,7 +108,7 @@ PARAM_UROW_IN("labels", "A matrix containing labels (0 or 1) for the points "
     "in the training set (y).", "l");
 
 // Optimizer parameters.
-PARAM_DOUBLE_IN("lambda", "L2-regularization parameter for training.", "L",
+PARAM_DOUBLE_IN("lambda", "L2-regularization parameter for training.", "r",
     0.0001);
 PARAM_DOUBLE_IN("delta", "Margin of difference between correct class and other "
     "classes.", "d", 1.0);
@@ -123,12 +123,12 @@ PARAM_DOUBLE_IN("tolerance", "Convergence tolerance for optimizer.", "e",
 PARAM_INT_IN("max_iterations", "Maximum iterations for optimizer (0 indicates "
     "no limit).", "n", 10000);
 PARAM_DOUBLE_IN("step_size", "Step size for parallel SGD optimizer.",
-    "s", 0.01);
+    "a", 0.01);
 PARAM_FLAG("shuffle", "Don't shuffle the order in which data points are "
     "visited for parallel SGD.", "S");
 PARAM_INT_IN("epochs", "Maximum number of full epochs over dataset for "
     "psgd", "E", 50);
-PARAM_INT_IN("seed", "Random seed.  If 0, 'std::time(NULL)' is used.", "r", 0);
+PARAM_INT_IN("seed", "Random seed.  If 0, 'std::time(NULL)' is used.", "s", 0);
 
 class LinearSVMModel
 {
@@ -390,9 +390,12 @@ static void mlpackMain()
     // Calculate accuracy, if desired.
     if (CLI::HasParam("test_labels"))
     {
-      arma::Row<size_t> testLabels =
+      arma::Row<size_t> testLabels;
+      arma::Row<size_t> testRawLabels =
           std::move(CLI::GetParam<arma::Row<size_t>>("test_labels"));
 
+      data::NormalizeLabels(testRawLabels, testLabels, model->mappings);
+
       if (testSet.n_cols != testLabels.n_elem)
       {
         Log::Fatal << "Test data given with " << PRINT_PARAM_STRING("test")
@@ -403,8 +406,11 @@ static void mlpackMain()
 
       numClasses = CLI::GetParam<int>("num_classes") == 0 ?
         model->mappings.n_elem : CLI::GetParam<int>("num_classes");
-      vector<size_t> correctClassCounts(numClasses, 0);
-      vector<size_t> labelSize(numClasses, 0);
+      arma::Col<size_t> correctClassCounts;
+      arma::Col<size_t> labelSize;
+      correctClassCounts.zeros(numClasses);
+      labelSize.zeros(numClasses);
+
       for (arma::uword i = 0; i != predictions.n_elem; ++i)
       {
         if (predictions(i) == testLabels(i))