Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs	(revision 9544)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkClassification.cs	(revision 9919)
@@ -26,11 +26,8 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 using HeuristicLab.Problems.DataAnalysis;
-using HeuristicLab.Problems.DataAnalysis.Symbolic;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
-using HeuristicLab.Parameters;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
@@ -47,5 +44,5 @@
     private const string NodesInSecondHiddenLayerParameterName = "NodesInSecondHiddenLayer";
     private const string RestartsParameterName = "Restarts";
-    private const string NeuralNetworkRegressionModelResultName = "Neural network classification solution";
+    private const string NeuralNetworkClassificationModelResultName = "Neural network classification solution";
 
     #region parameter properties
@@ -174,7 +171,7 @@
       double rmsError, avgRelError, relClassError;
       var solution = CreateNeuralNetworkClassificationSolution(Problem.ProblemData, HiddenLayers, NodesInFirstHiddenLayer, NodesInSecondHiddenLayer, Decay, Restarts, out rmsError, out avgRelError, out relClassError);
-      Results.Add(new Result(NeuralNetworkRegressionModelResultName, "The neural network regression solution.", solution));
-      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network regression solution on the training set.", new DoubleValue(rmsError)));
-      Results.Add(new Result("Average relative error", "The average of relative errors of the neural network regression solution on the training set.", new PercentValue(avgRelError)));
+      Results.Add(new Result(NeuralNetworkClassificationModelResultName, "The neural network classification solution.", solution));
+      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network classification solution on the training set.", new DoubleValue(rmsError)));
+      Results.Add(new Result("Average relative error", "The average of relative errors of the neural network classification solution on the training set.", new PercentValue(avgRelError)));
       Results.Add(new Result("Relative classification error", "The percentage of misclassified samples.", new PercentValue(relClassError)));
     }
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs	(revision 9544)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs	(revision 9919)
@@ -26,11 +26,8 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 using HeuristicLab.Problems.DataAnalysis;
-using HeuristicLab.Problems.DataAnalysis.Symbolic;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
-using HeuristicLab.Parameters;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
@@ -161,6 +158,6 @@
       var solution = CreateNeuralNetworkEnsembleClassificationSolution(Problem.ProblemData, EnsembleSize, HiddenLayers, NodesInFirstHiddenLayer, NodesInSecondHiddenLayer, Decay, Restarts, out rmsError, out avgRelError, out relClassError);
       Results.Add(new Result(NeuralNetworkEnsembleClassificationModelResultName, "The neural network ensemble classification solution.", solution));
-      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network ensemble regression solution on the training set.", new DoubleValue(rmsError)));
-      Results.Add(new Result("Average relative error", "The average of relative errors of the neural network ensemble regression solution on the training set.", new PercentValue(avgRelError)));
+      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network ensemble classification solution on the training set.", new DoubleValue(rmsError)));
+      Results.Add(new Result("Average relative error", "The average of relative errors of the neural network ensemble classification solution on the training set.", new PercentValue(avgRelError)));
       Results.Add(new Result("Relative classification error", "The percentage of misclassified samples.", new PercentValue(relClassError)));
     }
@@ -201,5 +198,5 @@
       int info;
       alglib.mlpetraines(mlpEnsemble, inputMatrix, nRows, decay, restarts, out info, out rep);
-      if (info != 6) throw new ArgumentException("Error in calculation of neural network ensemble regression solution");
+      if (info != 6) throw new ArgumentException("Error in calculation of neural network ensemble classification solution");
 
       rmsError = alglib.mlpermserror(mlpEnsemble, inputMatrix, nRows);
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs	(revision 9544)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleRegression.cs	(revision 9919)
@@ -26,11 +26,8 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 using HeuristicLab.Problems.DataAnalysis;
-using HeuristicLab.Problems.DataAnalysis.Symbolic;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
-using HeuristicLab.Parameters;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
