Name | Description | Type | Package | Framework |
CachedKernel | Base class for RBFKernel and PolyKernel that implements a simple LRU. | Class | weka.classifiers.functions.supportVector | Weka |
CheckKernel | Class for examining the capabilities and finding problems with kernels. | Class | weka.classifiers.functions.supportVector | Weka |
MultilayerPerceptron | A Classifier that uses backpropagation to classify This network can be built by hand, created by an algorithm or both. | Class | weka.classifiers.functions | Weka |
NeuralConnection | Abstract unit in a NeuralNetwork. | Class | weka.classifiers.functions.neural | Weka |
NeuralMethod | This is an interface used to create classes that can be used by the neuralnode to perform all it's computations. | Interface | weka.classifiers.functions.neural | Weka |
NeuralNode | This class is used to represent a node in the neuralnet. | Class | weka.classifiers.functions.neural | Weka |
NormalizedPolyKernel | The normalized polynomial kernel. | Class | weka.classifiers.functions.supportVector | Weka |
PolyKernel | The polynomial kernel : K(x, y) = | Class | weka.classifiers.functions.supportVector | Weka |
PrecomputedKernelMatrixKernel | This kernel is based on a static kernel matrix that is read from a file. | Class | weka.classifiers.functions.supportVector | Weka |
Puk | The Pearson VII function-based universal kernel. | Class | weka.classifiers.functions.supportVector | Weka |
RBFKernel | The RBF kernel. | Class | weka.classifiers.functions.supportVector | Weka |
RegOptimizer | Base class implementation for learning algorithm of SMOreg Valid options are: | Class | weka.classifiers.functions.supportVector | Weka |
RegSMO | Implementation of SMO for support vector regression A. | Class | weka.classifiers.functions.supportVector | Weka |
RegSMOImproved | Learn SVM for regression using SMO with Shevade, Keerthi, et al. | Class | weka.classifiers.functions.supportVector | Weka |
SGD | Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression). | Class | weka.classifiers.functions | Weka |
SGDText | Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data. | Class | weka.classifiers.functions | Weka |
SGDText .Count | Class | weka.classifiers.functions | Weka | |
SigmoidUnit | This can be used by the neuralnode to perform all it's computations (as a sigmoid unit). | Class | weka.classifiers.functions.neural | Weka |
SimpleLinearRegression | Learns a simple linear regression model. | Class | weka.classifiers.functions | Weka |
SimpleLogistic | Classifier for building linear logistic regression models. | Class | weka.classifiers.functions | Weka |
SMO | Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. | Class | weka.classifiers.functions | Weka |
SMOreg | SMOreg implements the support vector machine for regression. | Class | weka.classifiers.functions | Weka |
SMOset | Stores a set of integer of a given size. | Class | weka.classifiers.functions.supportVector | Weka |
VotedPerceptron | Implementation of the voted perceptron algorithm by Freund and Schapire. | Class | weka.classifiers.functions | Weka |