Package | Description |
---|---|
weka.classifiers.functions | |
weka.classifiers.functions.supportVector | |
weka.classifiers.mi | |
weka.classifiers.mi.supportVector | |
weka.filters.unsupervised.attribute |
Modifier and Type | Method and Description |
---|---|
Kernel |
SMO.getKernel()
Returns the kernel to use
|
Kernel |
SMO.BinarySMO.getKernel()
Returns the kernel to use
|
Kernel |
SMOreg.getKernel()
Returns the kernel to use
|
Kernel |
GaussianProcesses.getKernel()
Gets the kernel to use.
|
Modifier and Type | Method and Description |
---|---|
void |
SMO.setKernel(Kernel value)
sets the kernel to use
|
void |
SMO.BinarySMO.setKernel(Kernel value)
sets the kernel to use
|
void |
SMOreg.setKernel(Kernel value)
sets the kernel to use
|
void |
GaussianProcesses.setKernel(Kernel value)
Sets the kernel to use.
|
Modifier and Type | Class and Description |
---|---|
class |
CachedKernel
Base class for RBFKernel and PolyKernel that implements a simple LRU.
|
class |
NormalizedPolyKernel
The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y) Valid options are: |
class |
PolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
|
class |
PrecomputedKernelMatrixKernel
This kernel is based on a static kernel matrix that is read from a file.
|
class |
Puk
The Pearson VII function-based universal kernel.
For more information see: B. |
class |
RBFKernel
The RBF kernel.
|
class |
StringKernel
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. |
Modifier and Type | Method and Description |
---|---|
static Kernel |
Kernel.forName(String kernelName,
String[] options)
Creates a new instance of a kernel given it's class name and
(optional) arguments to pass to it's setOptions method.
|
Kernel |
CheckKernel.getKernel()
Get the kernel being tested
|
static Kernel[] |
Kernel.makeCopies(Kernel model,
int num)
Creates a given number of deep or shallow (if the kernel implements Copyable)
copies of the given kernel using serialization.
|
static Kernel |
Kernel.makeCopy(Kernel kernel)
Creates a shallow copy of the kernel (if it implements Copyable)
otherwise a deep copy using serialization.
|
Modifier and Type | Method and Description |
---|---|
String |
KernelEvaluation.evaluate(Kernel kernel,
Instances data)
Evaluates the Kernel with the given commandline options and returns
the evaluation string.
|
static String |
KernelEvaluation.evaluate(Kernel Kernel,
String[] options)
Evaluates the Kernel with the given commandline options and returns
the evaluation string.
|
static Kernel[] |
Kernel.makeCopies(Kernel model,
int num)
Creates a given number of deep or shallow (if the kernel implements Copyable)
copies of the given kernel using serialization.
|
static Kernel |
Kernel.makeCopy(Kernel kernel)
Creates a shallow copy of the kernel (if it implements Copyable)
otherwise a deep copy using serialization.
|
void |
CheckKernel.setKernel(Kernel value)
Set the lernel to test.
|
Modifier and Type | Method and Description |
---|---|
Kernel |
MISMO.getKernel()
Gets the kernel to use.
|
Kernel |
MISVM.getKernel()
Gets the kernel to use.
|
Modifier and Type | Method and Description |
---|---|
void |
MISMO.setKernel(Kernel value)
Sets the kernel to use.
|
void |
MISVM.setKernel(Kernel value)
Sets the kernel to use.
|
Modifier and Type | Class and Description |
---|---|
class |
MIPolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
|
class |
MIRBFKernel
The RBF kernel.
|
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelFilter.getKernel()
Gets the kernel to use.
|
Modifier and Type | Method and Description |
---|---|
void |
KernelFilter.setKernel(Kernel value)
Sets the kernel to use.
|
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