| weka.classifiers.functions.supportVector |
|
| Java Source File Name | Type | Comment |
| CachedKernel.java | Class | Base class for RBFKernel and PolyKernel that implements a simple LRU.
(least-recently-used) cache if the cache size is set to a value > 0.
Otherwise it uses a full cache.
author:
Eibe Frank (eibe@cs.waikato.ac.nz)
author:
Shane Legg (shane@intelligenesis.net) (sparse vector code)
author:
Stuart Inglis (stuart@reeltwo.com) (sparse vector code)
author:
J. |
| CheckKernel.java | Class | Class for examining the capabilities and finding problems with
kernels. |
| Kernel.java | Class | Abstract kernel. |
| KernelEvaluation.java | Class | Class for evaluating Kernels. |
| NormalizedPolyKernel.java | Class |
The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
Valid options are:
-D
Enables debugging output (if available) to be printed. |
| PolyKernel.java | Class |
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
-D
Enables debugging output (if available) to be printed. |
| Puk.java | Class |
The Pearson VII function-based universal kernel.
For more information see:
B. |
| RBFKernel.java | Class |
The RBF kernel. |
| RegOptimizer.java | Class | Base class implementation for learning algorithm of SVMreg
Valid options are:
-L <double>
The epsilon parameter in epsilon-insensitive loss function. |
| RegSMO.java | Class |
Implementation of SMO for support vector regression as described in :
A.J. |
| RegSMOImproved.java | Class |
Learn SVM for regression using SMO with Shevade, Keerthi, et al. |
| SMOset.java | Class | Stores a set of integer of a given size. |
| StringKernel.java | Class |
Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].
For more information, see
Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. |