diff --git a/src/mlpack/methods/linear_svm/linear_svm_impl.hpp b/src/mlpack/methods/linear_svm/linear_svm_impl.hpp
index 789cba8f1a..ff2876c441 100644
--- a/src/mlpack/methods/linear_svm/linear_svm_impl.hpp
+++ b/src/mlpack/methods/linear_svm/linear_svm_impl.hpp
@@ -95,6 +95,18 @@ const
 {
   Classify(data, scores);
 
+  #if ARMA_VERSION_MAJOR > 7 || ARMA_VERSION_MAJOR == 7 \
+  && ARMA_VERSION_MINOR >= 300
+
+  // Prepare necessary data
+  labels.zeros(data.n_cols);
+
+  labels = arma::conv_to<arma::Row<size_t>>::from(
+      arma::index_max(scores));
+
+  #else
+  // Once the minimum version is Armadillo is increased, remove this part.
+
   // Prepare necessary data
   labels.zeros(data.n_cols);
   double maxScore = 0;
@@ -116,6 +128,7 @@ const
     // Set maximum probability to zero for next input.
     maxScore = 0;
   }
+  #endif
 }
 
 template <typename MatType>
diff --git a/src/mlpack/tests/linear_svm_test.cpp b/src/mlpack/tests/linear_svm_test.cpp
index b06216191c..3f004714ee 100644
--- a/src/mlpack/tests/linear_svm_test.cpp
+++ b/src/mlpack/tests/linear_svm_test.cpp
@@ -573,9 +573,8 @@ BOOST_AUTO_TEST_CASE(LinearSVMFitIntercept)
   const double delta = 1.0;
 
   // Generate a two-Gaussian dataset,
-  // which can't be separated without adding the intercept term.
-  GaussianDistribution g1(arma::vec("1.0 1.0 1.0"), arma::eye<arma::mat>(3, 3));
-  GaussianDistribution g2(arma::vec("9.0 9.0 9.0"), arma::eye<arma::mat>(3, 3));
+  GaussianDistribution g1(arma::vec("1.0 9.0 1.0"), arma::eye<arma::mat>(3, 3));
+  GaussianDistribution g2(arma::vec("4.0 3.0 4.0"), arma::eye<arma::mat>(3, 3));
 
   arma::mat data(inputSize, points);
   arma::Row<size_t> labels(points);
@@ -710,7 +709,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMPSGDSimpleTest)
 
   // Compare training accuracy to 1.
   const double acc = lsvm.ComputeAccuracy(dataset, labels);
-  BOOST_REQUIRE_CLOSE(acc, 1.0, 0.5);
+  BOOST_REQUIRE_CLOSE(acc, 1.0, 1.0);
 }
 
 /**
@@ -756,7 +755,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMParallelSGDTwoClasses)
 
   // Compare training accuracy to 1.
   const double acc = lsvm.ComputeAccuracy(data, labels);
-  BOOST_REQUIRE_CLOSE(acc, 1.0, 0.5);
+  BOOST_REQUIRE_CLOSE(acc, 1.0, 1.0);
 
   // Create test dataset.
   for (size_t i = 0; i < points / 2; i++)
@@ -772,7 +771,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMParallelSGDTwoClasses)
 
   // Compare test accuracy to 1.
   const double testAcc = lsvm.ComputeAccuracy(data, labels);
-  BOOST_REQUIRE_CLOSE(testAcc, 1.0, 0.6);
+  BOOST_REQUIRE_CLOSE(testAcc, 1.0, 1.0);
 }
 
 #endif