Documentation of 'smile.math.kernel.SparseHyperbolicTangentKernel' Java class
SparseHyperbolicTangentKernel
smile.math.kernel

Class SparseHyperbolicTangentKernel

  • All Implemented Interfaces:
    java.io.Serializable, MercerKernel<SparseArray>


    public class SparseHyperbolicTangentKernel
    extends java.lang.Object
    implements MercerKernel<SparseArray>, java.io.Serializable
    The hyperbolic tangent kernel. k(u, v) = tanh(γ uTv - λ), where γ is the scale of the used inner product and λ is the offset of the used inner product. If the offset is negative the likelihood of obtaining a kernel matrix that is not positive definite is much higher (since then even some diagonal elements may be negative), hence if this kernel has to be used, the offset should always be positive. Note, however, that this is no guarantee that the kernel will be positive.

    The hyperbolic tangent kernel was quite popular for support vector machines due to its origin from neural networks. However, it should be used carefully since the kernel matrix may not be positive semi-definite. Besides, it was reported the hyperbolic tangent kernel is not better than the Gaussian kernel in general..

    See Also:
    Serialized Form
    • Constructor Detail

      • SparseHyperbolicTangentKernel

        public SparseHyperbolicTangentKernel()
        Constructor with scale 1.0 and offset 0.0.
      • SparseHyperbolicTangentKernel

        public SparseHyperbolicTangentKernel(double scale,
                                             double offset)
        Constructor.

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