| AdpBP.h [code] | Offers the two versions of the gradient descent based optimization algorithm with individual adaptive learning rates by Silva and Almeida (Adaptive BackPropagation) |
| ArtificialDistributions.cpp [code] | Artificial benchmark data |
| ArtificialDistributions.h [code] | Artificial benchmark data |
| BFGS.cpp [code] | Offers the Broyden-Fletcher-Goldfarb-Shanno algorithm for the optimization of models |
| BFGS.h [code] | Offers the Broyden-Fletcher-Goldfarb-Shanno algorithm for the optimization of models |
| BinaryCriterion.h [code] | Uses thresholds for transforming output and target vectors into binary vectors and then calculates the classification error |
| CG.cpp [code] | Offers the Conjugate Gradients algorithm for the optimization of models |
| CG.h [code] | Offers the Conjugate Gradients algorithm for the optimization of models |
| ClassificationError.cpp [code] | Compute the fraction of classification errors |
| ClassificationError.h [code] | Compute the fraction of classification errors |
| CMAOptimizer.cpp [code] | The CMA-ES as a ReClaM Optimizer |
| CMAOptimizer.h [code] | The CMA-ES as a ReClaM Optimizer |
| ComponentWiseModel.cpp [code] | The ComponentWiseModel encapsulates the component wise application of a base model |
| ComponentWiseModel.h [code] | The ComponentWiseModel encapsulates the component wise application of a base model |
| ConcatenatedModel.cpp [code] | The ConcatenatedModel encapsulates a chain of basic models |
| ConcatenatedModel.h [code] | The ConcatenatedModel encapsulates a chain of basic models |
| CoverTree.cpp [code] | |
| CoverTree.h [code] | |
| createConnectionMatrix.h [code] | Offers methods for creating connection matrices for neural networks |
| CrossEntropy.h [code] | Error measure for classication tasks that can be used as the objective function for training |
| CrossEntropyIndependent.h [code] | Error measure for classification tasks of non exclusive attributes that can be used for model training |
| CrossValidation.cpp [code] | Cross Validation |
| CrossValidation.h [code] | Cross Validation |
| Dataset.cpp [code] | Functions for loading ReClaM datasets |
| Dataset.h [code] | Functions for loading ReClaM datasets |
| DF_CrossEntropy.h [code] | Error measure for classication tasks that can be used as the objective function for training |
| DF_CrossEntropyIndependent.h [code] | Error measure for classification tasks of non exclusive attributes that can be used for model training |
| DF_MeanSquaredError.h [code] | Mean Squared Error, using the GeneralDerivative interface |
| Documentation [code] | |
| EarlyStopping.cpp [code] | Used for monitoring purposes, to avoid overfitting |
| EarlyStopping.h [code] | Used for monitoring purposes, to avoid overfitting |
| ErrorFunction.cpp [code] | Base class of all error measures |
| ErrorFunction.h [code] | Base class of all error measures |
| ErrorPercentage.h [code] | Calculates the error percentage based on the mean squared error |
| FFNet.cpp [code] | Offers the functions to create and to work with a feed-forward network |
| FFNet.h [code] | Offers the functions to create and to work with a feed-forward network |
| FFNetSource.h [code] | |
| GaussianProcess.cpp [code] | Gaussian Process implementation |
| GaussianProcess.h [code] | Gaussian Process header |
| GaussKernel.cpp [code] | Gauss kernels with adaptive covariance matrices |
| GaussKernel.h [code] | Gauss kernels with adaptive covariance matrices |
| GridSearch.cpp [code] | Optimization by grid or point set search |
| GridSearch.h [code] | Optimization by grid or point set search |
| InverseClassSeparability.cpp [code] | Inverse of the Class Separability Measure J by Huilin Xiong and M. N. S. Swamy |
| InverseClassSeparability.h [code] | Inverse of the Class Separability Measure J by Huilin Xiong and M. N. S. Swamy |
| JaakkolaHeuristic.cpp [code] | Jaakkola's heuristic and related quantities for Gaussian kernel selection |
| JaakkolaHeuristic.h [code] | Jaakkola's heuristic and related quantities for Gaussian kernel selection |
| KalmanFilter.cpp [code] | Standard linear Kalman filter |
| KalmanFilter.h [code] | Standard linear Kalman filter |
| KernelFunction.cpp [code] | Kernel function base class and simple function implementations |
| KernelFunction.h [code] | This file contains the definition of a kernel function as well as basic examples of kernel functions |
| KernelKMeans.cpp [code] | Kernel k-means clustering |
| KernelKMeans.h [code] | Kernel k-means clustering |
| KernelMeanClassifier.cpp [code] | Kernel Mean Classifier |
| KernelMeanClassifier.h [code] | Kernel Mean Classifier |
| KernelNearestNeighbor.cpp [code] | Kernel k-Nearest Neighbor Classifier |
| KernelNearestNeighbor.h [code] | Kernel k-Nearest Neighbor Classifier |
| KTA.cpp [code] | Implementation of the (negative) Kernel Target Alignment (KTA) as proposed by Nello Cristianini |
| KTA.h [code] | Implementation of the (negative) Kernel Target Alignment (KTA) as proposed by Nello Cristianini |
| LDA.cpp [code] | Train a LinearClassifier using Linear Discriminant Analysis (LDA) |
| LDA.h [code] | Linear Discriminant Analysis (LDA) |
| LinearEquation.cpp [code] | Model and Error Function for the iterative approximate solution of a linear system |
| LinearEquation.h [code] | Model and Error Function for the iterative approximate solution of a linear system |
| LinearModel.cpp [code] | Linear models on a real vector space |
| LinearModel.h [code] | Linear models on a real vector space |
| LinearRegression.cpp [code] | Linear Regression |
| LinearRegression.h [code] | Linear Regression |
| LinOutFFNet.h [code] | Offers the functions to create and to work with (F)eed-(F)orward (Net)works with (Lin)ear (Out)put |
| LinOutMSEBFFNet.h [code] | Offers the functions to create and to work with (F)eed-(F)orward (Net)works with (Lin)ear (Out)put |
| LMSEFFNet.h [code] | Offers the functions to create and to work with a feed-forward network with explicit defined neuron-to-bias connections. The network is combined with the mean squared error measure. This combination is created due to computational efficiency |
| LOO.cpp [code] | Leave One Out (LOO) Error for Support Vector Machines |
| LOO.h [code] | Leave One Out (LOO) Error for Support Vector Machines |
| MeanSquaredError.h [code] | Calculates the mean squared error |
| Model.cpp [code] | Base class of all models |
| Model.h [code] | Base class of all models |
| ModelInterface.h [code] | [DEPRECATED] Realizes the communication between the different modules |
| ModelWithErrorFunction.h [code] | Abstarct base class for objects which are both models and error functions |
| MSEFFNet.cpp [code] | Offers the functions to create and to work with a feed-forward network combined with the mean squared error measure. This combination is created due to computational efficiency |
| MSEFFNet.h [code] | Offers the functions to create and to work with a feed-forward network combined with the mean squared error measure. This combination is created due to computational efficiency |
| MSERBFNet.h [code] | Offers the functions to create and to work with radial basis function networks and to train it with the mean squared error. This combination provides more computational efficiency compared to using the class MeanSquaredError |
| MSERNNet.cpp [code] | Mean Squared Error Recurrent Neural Network |
| MSERNNet.h [code] | Offers the functions to create and to work with a recurrent neural network. The network is combined with the mean squared error measure. This combination is created due to computational efficiency |
| NegativeLogLikelihood.cpp [code] | Negative logarithm of the likelihood of a probabilistic binary classification model |
| NegativeLogLikelihood.h [code] | Negative logarithm of the likelihood of a probabilistic binary classification model |
| NegativePolarization.cpp [code] | Implementation of the negative Kernel Polarization Measure, that is, Kernel Target Alignment without normalization |
| NegativePolarization.h [code] | Implementation of the negative Kernel Polarization Measure, that is, Kernel Target Alignment without normalization |
| NetParams.cpp [code] | Offers functions for easily reading information about a network, an error measure and an optimization algorithm from a configuration file |
| NetParams.h [code] | Easily configuration file reading for neural networks |
| NoisyRprop.cpp [code] | Rprop for noisy function evaluations |
| NoisyRprop.h [code] | Rprop for noisy function evaluations |
| NoisySvmLikelihood.cpp [code] | Model selection objective function for SVMs |
| NoisySvmLikelihood.h [code] | Model selection objective function for SVMs |
| NormedModels.h [code] | Wrapper models normalizing the output |
| Optimizer.cpp [code] | Base class of all optimizers |
| Optimizer.h [code] | Base class of all optimizers |
| Paraboloid.h [code] | Convex quadratic model and error function |
| PCA.cpp [code] | Train a (affine) linear map using Principal Component Analysis (PCA) |
| PCA.h [code] | Principal Component Analysis (PCA) |
| Perceptron.cpp [code] | Perceptron online learning algorith, |
| Perceptron.h [code] | Perceptron online learning algorith |
| ProbenBNet.h [code] | Offers a predefined feed-forward neural network |
| ProbenNet.h [code] | Offers a predefined feed-forward neural network |
| QuadraticProgram.cpp [code] | Quadratic programming for Support Vector Machines |
| QuadraticProgram.h [code] | Quadratic programming for Support Vector Machines |
| Quickprop.h [code] | This file includes the popular heuristic optimization method named "Quickprop" |
| RadiusMargin.cpp [code] | Squared radius margin quotient |
| RadiusMargin.h [code] | Squared radius margin quotient |
| RBFNet.h [code] | Offers the functions to create and to work with radial basis function networks |
| ROC.cpp [code] | Computes a "receiver operator characteristics" curve |
| ROC.h [code] | Computes a "receiver operator characteristics" curve |
| Rprop.h [code] | This file offers classes to use the Resilient-Backpropagation-algorithm for the optimization of the adaptive parameters of a network |
| SigmoidFit.cpp [code] | Optimization of the SigmoidModel according to Platt, 1999 |
| SigmoidFit.h [code] | Optimization of the SigmoidModel according to Platt, 1999 |
| SigmoidModel.cpp [code] | Sigmoidal functions |
| SigmoidModel.h [code] | Sigmoidal functions |
| SpanBound.cpp [code] | Compute the SpanBound for the 2-norm SVM |
| SpanBound.h [code] | SpanBound for the 2-norm SVM |
| SpanBound1.cpp [code] | Compute the SpanBound for the 1-norm SVM |
| SpanBound1.h [code] | SpanBound for the 1-norm SVM |
| SquaredError.h [code] | Calculates the sum-of-squares error |
| SteepestDescent.h [code] | The simplest learning strategy |
| StochasticGradientDescent.h [code] | Learning strategies based on SteepestDescent, but with randomly chosen patterns |
| Svm.cpp [code] | Support Vector Machine implementation |
| Svm.h [code] | Support Vector Machine interface |
| SvmApproximation.cpp [code] | Approximation of Support Vector Machines (SVMs) |
| SvmApproximation.h [code] | Approximation of Support Vector Machines (SVMs) |
| TanhNet.h [code] | Offers a predefined feed-forward neural network |
| ValidationError.cpp [code] | Compute the error on a hold out set |
| ValidationError.h [code] | Compute the error on a hold out set |
| Variance.h [code] | Cariance of data in each column of a data set |
| VarianceEstimator.cpp [code] | Offers the methods to deal with active learning of a neural network |
| VarianceEstimator.h [code] | Offers the methods to deal with active learning of a neural network |
| WTA.h [code] | Winner-takes-all error function |