MATLAB File Help: prtKernelHyperbolicTangent
  prtKernelHyperbolicTangent  Hyperbolic tangent kernel
   kernelObj = prtKernelHyperbolicTangent; Generates a kernel object
   implementing a hyperbolic tangent.  Kernel objects are widely used
   in several prt classifiers, such as prtClassRvm and prtClassSvm.
   Hyperbolic tangent kernels implement the following function for 1 x
   N vectors x1 and x2:
    k(x1,x2) = tanh(kappa*x1*x2'+c);
   KERNOBJ = prtKernelHyperbolicTangent(PROPERTY1, VALUE1, ...) constructs a
   prtKernelHyperbolicTangent object KERNOBJ with properties as specified by
   PROPERTY/VALUE pairs. prtKernelHyperbolicTangent objects have the following
   user-settable properties:
    kappa   - Positive scalar value specifying the gain on the inner
              product between x1 and x2 (default 1)
    c       - Scalar value specifying DC offset in hyperbolic tangent
   For more information on these kernels, please refer to:
   prtKernelHyperbolicTangent objects inherit the TRAIN, RUN, and AND
   methods from prtKernel.
   % Example:
   ds = prtDataGenBimodal;
   k1 = prtKernelHyperbolicTangent;
   k2 = prtKernelHyperbolicTangent('kappa',2);
   k1 = k1.train(ds); % Train
   g1 =; % Evaluate
   k2 = k2.train(ds); % Train
   g2 =; % Evaluate
   subplot(2,2,1); imagesc(g1.getObservations);  %Plot the results
   subplot(2,2,2); imagesc(g2.getObservations);
See also
Class Details
Superclasses prtKernel
Sealed false
Construct on load false
Constructor Summary
prtKernelHyperbolicTangent Hyperbolic tangent kernel 
Property Summary
c offset 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isCrossValidateValid False 
isTrained Indicates if prtAction object has been trained. 
kappa polynomial order 
name Hyperbolic Tangent Kernel 
nameAbbreviation TANH 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  and Combine 2 prtKernels into a prtKernelSet 
  get get the object properties 
  optimize Optimize action parameter by exhaustive function maximization. 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.