Package smile.anomaly

Class SVM<T>

java.lang.Object

smile.base.svm.KernelMachine<T>

smile.anomaly.SVM<T>

Type Parameters:

T - the data type of model input objects.

All Implemented Interfaces:

Serializable

public class SVM<T>
extends KernelMachine<T>

One-class support vector machines for novelty detection. One-class SVM relies on identifying the smallest hypersphere consisting of all the data points. Therefore, it is sensitive to outliers. If the training data is not contaminated by outliers, the model is best suited for novelty detection.

References

1. B. Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 2001.
2. Jia Jiong and Zhang Hao-ran. A Fast Learning Algorithm for One-Class Support Vector Machine. ICNC 2007.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
SVM(MercerKernel<T> kernel, T[] vectors, double[] weight, double b)	Constructor.

Method Summary

Modifier and Type	Method	Description
static <T> SVM<T>	fit(T[] x, MercerKernel<T> kernel)	Fits a one-class SVM.
static <T> SVM<T>	fit(T[] x, MercerKernel<T> kernel, double nu, double tol)	Fits a one-class SVM.

Methods inherited from class smile.base.svm.KernelMachine

intercept, kernel, score, toString, vectors, weights

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Details

SVM

public SVM(MercerKernel<T> kernel,
 T[] vectors,
 double[] weight,
 double b)

Constructor.

Parameters:

kernel - Kernel function.

vectors - The support vectors.

weight - The weights of instances.

b - The intercept;

Method Details

fit

public static <T> SVM<T> fit(T[] x,
 MercerKernel<T> kernel)

Fits a one-class SVM.

Type Parameters:

T - the data type.

Parameters:

x - training samples.

kernel - the kernel function.

Returns:

the model.

fit

public static <T> SVM<T> fit(T[] x,
 MercerKernel<T> kernel,
 double nu,
 double tol)

Fits a one-class SVM.

Type Parameters:

T - the data type.

Parameters:

x - training samples.

kernel - the kernel function.

nu - the parameter sets an upper bound on the fraction of outliers (training examples regarded out-of-class) and it is a lower bound on the number of training examples used as Support Vector.

tol - the tolerance of convergence test.

Returns:

the model.