SupportVectorMachineTKernel Class 
Namespace: Accord.MachineLearning.VectorMachines
[SerializableAttribute] public class SupportVectorMachine<TKernel> : SupportVectorMachine<TKernel, double[]> where TKernel : Object, IKernel<double[]>
The SupportVectorMachineTKernel type exposes the following members.
Name  Description  

SupportVectorMachineTKernel 
Initializes a new instance of the SupportVectorMachineTKernel class.

Name  Description  

Inputs  Obsolete.
Gets the number of inputs accepted by this machine.
(Inherited from SupportVectorMachineTKernel, TInput.)  
IsCompact  Obsolete.
Obsolete.
(Inherited from SupportVectorMachineTKernel, TInput.)  
IsProbabilistic 
Gets whether this machine has been calibrated to
produce probabilistic outputs (through the Probability(TInput)
method).
(Inherited from SupportVectorMachineTKernel, TInput.)  
Kernel 
Gets or sets the kernel used by this machine.
(Inherited from SupportVectorMachineTKernel, TInput.)  
NumberOfInputs 
Gets the number of inputs accepted by the model.
(Inherited from TransformBaseTInput, TOutput.)  
NumberOfOutputs 
Gets the number of outputs generated by the model.
(Inherited from TransformBaseTInput, TOutput.)  
SupportVectors 
Gets or sets the collection of support vectors used by this machine.
(Inherited from SupportVectorMachineTKernel, TInput.)  
Threshold 
Gets or sets the threshold (bias) term for this machine.
(Inherited from SupportVectorMachineTKernel, TInput.)  
Weights 
Gets or sets the collection of weights used by this machine.
(Inherited from SupportVectorMachineTKernel, TInput.) 
Name  Description  

Clone 
Creates a new object that is a copy of the current instance.
(Overrides SupportVectorMachineTKernel, TInputClone.)  
Compress 
If this machine has a linear kernel, compresses all
support vectors into a single parameter vector.
(Inherited from SupportVectorMachineTKernel, TInput.)  
Compute(TInput)  Obsolete.
Computes the given input to produce the corresponding output.
(Inherited from SupportVectorMachineTKernel, TInput.)  
Compute(TInput, Double)  Obsolete.
Computes the given input to produce the corresponding output.
(Inherited from SupportVectorMachineTKernel, TInput.)  
Decide(TInput) 
Computes classlabel decisions for a given set of input vectors.
(Inherited from ClassifierBaseTInput, TClasses.)  
Decide(TInput) 
Computes a classlabel decision for a given input.
(Inherited from SupportVectorMachineTKernel, TInput.)  
Decide(TInput, Boolean) 
Computes classlabel decisions for the given input.
(Inherited from BinaryClassifierBaseTInput.)  
Decide(TInput, TClasses) 
Computes a classlabel decision for a given input.
(Inherited from ClassifierBaseTInput, TClasses.)  
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
LogLikelihood(TInput) 
Predicts a class label vector for the given input vector, returning the
loglikelihood that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput) 
Predicts a class label vector for the given input vector, returning the
loglikelihood that the input vector belongs to its predicted class.
(Inherited from SupportVectorMachineTKernel, TInput.)  
LogLikelihood(TInput, Boolean) 
Predicts a class label for each input vector, returning the
loglikelihood that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput, Double) 
Predicts a class label vector for the given input vector, returning the
loglikelihood that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput, Boolean) 
Predicts a class label vector for the given input vector, returning the
loglikelihood that the input vector belongs to its predicted class.
(Inherited from SupportVectorMachineTKernel, TInput.)  
LogLikelihood(TInput, Boolean, Double) 
Predicts a class label for each input vector, returning the
loglikelihood that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Boolean) 
Predicts a class label vector for the given input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Boolean) 
Predicts a class label vector for each input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Boolean, Double) 
Predicts a class label vector for the given input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Boolean, Double) 
Predicts a class label vector for each input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
Probabilities(TInput, Boolean) 
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Boolean) 
Predicts a class label vector for each input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Boolean, Double) 
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Boolean, Double) 
Predicts a class label vector for each input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probability(TInput) 
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probability(TInput) 
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probability(TInput, Boolean) 
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probability(TInput, Boolean) 
Predicts a class label for each input vector, returning the
probability that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probability(TInput, Double) 
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Probability(TInput, Boolean, Double) 
Predicts a class label for each input vector, returning the
probability that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Score(TInput) 
Computes a numerical score measuring the association between
the given input vector and its most strongly
associated class (as predicted by the classifier).
(Inherited from BinaryScoreClassifierBaseTInput.)  
Score(TInput) 
Computes a numerical score measuring the association between
the given input vector and its most strongly
associated class (as predicted by the classifier).
(Inherited from SupportVectorMachineTKernel, TInput.)  
Score(TInput, Boolean) 
Predicts a class label for the input vector, returning a
numerical score measuring the strength of association of the
input vector to its most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Score(TInput, Boolean) 
Predicts a class label for each input vector, returning a
numerical score measuring the strength of association of the
input vector to the most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Score(TInput, Double) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Score(TInput, Boolean, Double) 
Predicts a class label for each input vector, returning a
numerical score measuring the strength of association of the
input vector to the most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput, Boolean) 
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput, Double) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput, Boolean) 
Predicts a class label vector for each input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput, Double) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput, Boolean, Double) 
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)  
Scores(TInput, Boolean, Double) 
Predicts a class label vector for each input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)  
ToMulticlass 
Views this instance as a multiclass generative classifier,
giving access to more advanced methods, such as the prediction
of integer labels.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
ToMultilabel 
Views this instance as a multilabel generative classifier,
giving access to more advanced methods, such as the prediction
of onehot vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
ToString  Returns a string that represents the current object. (Inherited from Object.)  
ToWeights 
Converts a Linearkernel machine into an array of
linear coefficients. The first position in the array is the
Threshold value. If this
machine is not linear, an exception will be thrown.
(Overrides SupportVectorMachineTKernel, TInputToWeights.)  
Transform(TInput) 
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.)  
Transform(TInput) 
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from TransformBaseTInput, TOutput.)  
Transform(TInput, Boolean) 
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)  
Transform(TInput, Int32) 
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)  
Transform(TInput, Boolean) 
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)  
Transform(TInput, Int32) 
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)  
Transform(TInput, Int32) 
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)  
Transform(TInput, Double) 
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Transform(TInput, Double) 
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Transform(TInput, Double) 
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)  
Transform(TInput, TClasses) 
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual  Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by Matrix.) 
The original optimal hyperplane algorithm (SVM) proposed by Vladimir Vapnik in 1963 was a linear classifier. However, in 1992, Bernhard Boser, Isabelle Guyon and Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick (originally proposed by Aizerman et al.) to maximummargin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function.
This allows the algorithm to fit the maximummargin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high dimensional; thus though the classifier is a hyperplane in the highdimensional feature space, it may be nonlinear in the original input space.
The machines are also able to learn sequence classification problems in which the input vectors can have arbitrary length. For an example on how to do that, please see the documentation page for the DynamicTimeWarping kernel.
References:
The first example shows how to learn an SVM using a standard kernel that operates on vectors of doubles.
// As an example, we will try to learn a decision machine // that can replicate the "exclusiveor" logical function: double[][] inputs = { new double[] { 0, 0 }, // the XOR function takes two booleans new double[] { 0, 1 }, // and computes their exclusive or: the new double[] { 1, 0 }, // output is true only if the two booleans new double[] { 1, 1 } // are different }; int[] xor = // this is the output of the xor function { 0, // 0 xor 0 = 0 (inputs are equal) 1, // 0 xor 1 = 1 (inputs are different) 1, // 1 xor 0 = 1 (inputs are different) 0, // 1 xor 1 = 0 (inputs are equal) }; // Now, we can create the sequential minimal optimization teacher var learn = new SequentialMinimalOptimization<Gaussian>() { UseComplexityHeuristic = true, UseKernelEstimation = true }; // And then we can obtain a trained SVM by calling its Learn method SupportVectorMachine<Gaussian> svm = learn.Learn(inputs, xor); // Finally, we can obtain the decisions predicted by the machine: bool[] prediction = svm.Decide(inputs);
The second example shows how to learn an SVM using a Sparse kernel that operates on sparse vectors.
// As an example, we will try to learn a decision machine // that can replicate the "exclusiveor" logical function: Sparse<double>[] inputs = { Sparse.FromDense(new double[] { 0, 0 }), // the XOR function takes two booleans Sparse.FromDense(new double[] { 0, 1 }), // and computes their exclusive or: the Sparse.FromDense(new double[] { 1, 0 }), // output is true only if the two booleans Sparse.FromDense(new double[] { 1, 1 }) // are different }; int[] xor = // this is the output of the xor function { 0, // 0 xor 0 = 0 (inputs are equal) 1, // 0 xor 1 = 1 (inputs are different) 1, // 1 xor 0 = 1 (inputs are different) 0, // 1 xor 1 = 0 (inputs are equal) }; // Now, we can create the sequential minimal optimization teacher var learn = new SequentialMinimalOptimization<Gaussian, Sparse<double>>() { UseComplexityHeuristic = true, UseKernelEstimation = true }; // And then we can obtain a trained SVM by calling its Learn method var svm = learn.Learn(inputs, xor); // Finally, we can obtain the decisions predicted by the machine: bool[] prediction = svm.Decide(inputs);