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AveragedStochasticGradientDescentTKernel Properties |
The AveragedStochasticGradientDescentTKernel generic type exposes the following members.
Properties| Name | Description | |
|---|---|---|
 | CurrentEpoch |
Gets or sets the current epoch counter.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| Iterations | Obsolete.
Please use MaxIterations instead.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| Kernel |
Gets or sets the kernel function use to create a
kernel Support Vector Machine.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| Lambda |
Gets or sets the lambda regularization term. Default is 0.5.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| LearningRate |
Gets or sets the learning rate for the SGD algorithm.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| Loss |
Gets or sets the loss function to be used.
Default is to use the LogisticLoss.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| MaxIterations |
Gets or sets the number of iterations that should be
performed by the algorithm when calling Learn(TInput, Boolean, Double).
Default is 0 (iterate until convergence).
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| Model |
Gets or sets the classifier being learned.
(Inherited from BinaryLearningBaseTModel, TInput.) |
| ParallelOptions |
Gets or sets the parallelization options for this algorithm.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| Token |
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
| Tolerance |
Gets or sets the maximum relative change in the watched value
after an iteration of the algorithm used to detect convergence.
Default is 1e-3. If set to 0, the loss will not be computed
during learning and execution will be faster.
(Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.) |
See Also