templateKernel
Description
templateKernel
creates a template suitable for fitting a
Gaussian kernel classification model for nonlinear classification.
The template specifies the binary learner model, number of dimensions of expanded space,
kernel scale, box constraint, and regularization strength, among other parameters. After
creating the template, train the model by passing the template and data to fitcecoc
.
returns a kernel model
template.t
= templateKernel()
If you create a default template, then the software uses default values for all input arguments during training.
returns a template with additional options specified by one or more name-value pair
arguments. For example, you can implement logistic regression or specify the number of
dimensions of the expanded space.t
= templateKernel(Name,Value
)
If you display t
in the Command Window, then some properties of
t
appear empty ([]
). During training, the software
uses default values for the empty properties.
Examples
Input Arguments
Output Arguments
More About
Algorithms
templateKernel
minimizes the regularized objective function using a Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) solver with ridge (L2) regularization. To find the type of LBFGS solver used for training, type FitInfo.Solver
in the Command Window.
'LBFGS-fast'
— LBFGS solver.'LBFGS-blockwise'
— LBFGS solver with a block-wise strategy. IftemplateKernel
requires more memory than the value ofBlockSize
to hold the transformed predictor data, then the function uses a block-wise strategy.'LBFGS-tall'
— LBFGS solver with a block-wise strategy for tall arrays.
When templateKernel
uses a block-wise strategy, it implements LBFGS by
distributing the calculation of the loss and gradient among different parts of the data at
each iteration. Also, templateKernel
refines the initial estimates of the
linear coefficients and the bias term by fitting the model locally to parts of the data and
combining the coefficients by averaging. If you specify 'Verbose',1
, then
templateKernel
displays diagnostic information for each data pass and
stores the information in the History
field of
FitInfo
.
When templateKernel
does not use a block-wise strategy, the initial estimates are zeros. If you specify 'Verbose',1
, then templateKernel
displays diagnostic information for each iteration and stores the information in the History
field of FitInfo
.
References
[3] Huang, P. S., H. Avron, T. N. Sainath, V. Sindhwani, and B. Ramabhadran. “Kernel methods match Deep Neural Networks on TIMIT.” 2014 IEEE International Conference on Acoustics, Speech and Signal Processing. 2014, pp. 205–209.
Extended Capabilities
Version History
Introduced in R2018b