Constructor and Description |
---|
PairSimilarityExtractor(String similarityVectorName,
Kernel... kernel) |
Modifier and Type | Method and Description |
---|---|
Kernel |
VectorBasedStructureElementSimilarity.getKernel()
Returns the kernel used in comparing two vectors
|
Modifier and Type | Method and Description |
---|---|
void |
VectorBasedStructureElementSimilarity.setKernel(Kernel kernel)
Sets the kernel to be used in comparing two vectors
|
Modifier and Type | Class and Description |
---|---|
class |
DirectKernel<T extends Representation>
It is a kernel that operates exploiting directly on a specific
representation.
|
class |
KernelCombination
It is a kernel that operates combining other kernels
|
class |
KernelComposition
It is a kernel that operates enriching the computation performed by another kernel
|
Modifier and Type | Field and Description |
---|---|
protected Kernel |
KernelComposition.baseKernel |
Modifier and Type | Field and Description |
---|---|
protected List<Kernel> |
KernelCombination.toCombine |
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelComposition.getBaseKernel()
Returns the kernel this kernel is enriching
|
static Kernel |
Kernel.load(String inputFilePath)
Load a kernel function from a file path.
|
Modifier and Type | Method and Description |
---|---|
List<Kernel> |
KernelCombination.getToCombine()
Returns a list of the kernels this kernel is combining
|
Modifier and Type | Method and Description |
---|---|
static void |
Kernel.save(Kernel kernel,
String outputFilePath)
Save the input kernel in a file.
|
void |
KernelComposition.setBaseKernel(Kernel baseKernel) |
Modifier and Type | Method and Description |
---|---|
void |
KernelCombination.setToCombine(List<Kernel> toCombine) |
Modifier and Type | Class and Description |
---|---|
class |
KernelOverPairs
It is a kernel operating on ExamplePairs applying a simpler kernel to the pair elements
|
class |
PreferenceKernel
It is a kernel operating on ExamplePairs applying the following formula:
\(K( < x_1, x_2 >, < y_1,y_2 > ) = K(x_1, y_1) + K(x_2, y_2) - K(x_1, y_2) - K(x_2, y_1)\) where K is another kernel the preference kernel relies on. |
Constructor and Description |
---|
PreferenceKernel(Kernel baseKernel) |
Modifier and Type | Class and Description |
---|---|
class |
SequenceKernel
Sequence Kernel implementation.
|
Modifier and Type | Class and Description |
---|---|
class |
LinearKernelCombination
Weighted Linear Combination of Kernels
Given a kernel some kernel K1...Km, with weights c1,...cn, the combination formula is: SUM(Ki*ci) |
class |
NormalizationKernel
Normalization of a generic kernel K
Normalization formula: \(K(x,y) = \frac{K(x,y)}{\sqrt{(K(x,x) \cdot K(y,y))}}\)
|
class |
PolynomialKernel |
class |
RbfKernel
Radial Basis Function Kernel.
|
Modifier and Type | Method and Description |
---|---|
void |
LinearKernelCombination.addKernel(float weight,
Kernel kernel)
Adds a kernel with a corresponding weight to the linear combination of kernels
|
Constructor and Description |
---|
NormalizationKernel(Kernel kernelToNormalize) |
PolynomialKernel(float degree,
float a,
float b,
Kernel inputSpace) |
PolynomialKernel(float degree,
Kernel inputSpace) |
RbfKernel(float gamma,
Kernel inputSpace) |
Modifier and Type | Class and Description |
---|---|
class |
PartialTreeKernel
Partial Tree Kernel implementation.
|
class |
SmoothedPartialTreeKernel
Partial Tree Kernel implementation.
|
class |
SubSetTreeKernel
A SubSetTree Kernel is a convolution kernel that evaluates the tree fragments
shared between two trees.
|
class |
SubTreeKernel
SubTree Kernel implementation.
|
Modifier and Type | Class and Description |
---|---|
class |
LinearKernel
Linear Kernel for
Vector s It executes the dot product between two Vector representations |
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelMethod.getKernel()
Returns the kernel exploited by this learner
|
Modifier and Type | Method and Description |
---|---|
void |
KernelMethod.setKernel(Kernel kernel)
Sets the kernel this
|
Modifier and Type | Method and Description |
---|---|
Kernel |
Stoptron.getKernel() |
Kernel |
RandomizedBudgetPerceptron.getKernel() |
Modifier and Type | Method and Description |
---|---|
void |
Stoptron.setKernel(Kernel kernel) |
void |
RandomizedBudgetPerceptron.setKernel(Kernel kernel) |
Modifier and Type | Method and Description |
---|---|
void |
BinaryNuSvmClassification.setKernel(Kernel kernel) |
void |
BinaryCSvmClassification.setKernel(Kernel kernel) |
Constructor and Description |
---|
BinaryCSvmClassification(Kernel kernel,
Label label,
float cp,
float cn) |
BinaryCSvmClassification(Kernel kernel,
Label label,
float cp,
float cn,
boolean useFairness) |
BinaryNuSvmClassification(Kernel kernel,
Label label,
float nu) |
OneClassSvmClassification(Kernel kernel,
Label label,
float nu) |
Modifier and Type | Field and Description |
---|---|
protected Kernel |
LibSvmSolver.kernel
The Kernel function between examples, i.e.
|
Modifier and Type | Method and Description |
---|---|
Kernel |
LibSvmSolver.getKernel() |
Constructor and Description |
---|
LibCSvmSolver(Kernel kernel,
float cp,
float cn) |
LibNuSvmSolver(Kernel kernel,
int cp,
int cn) |
LibSvmSolver(Kernel kernel,
float Cp,
float Cn) |
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelizedPassiveAggressiveClassification.getKernel() |
Kernel |
BudgetedPassiveAggressiveClassification.getKernel() |
Modifier and Type | Method and Description |
---|---|
void |
KernelizedPassiveAggressiveClassification.setKernel(Kernel kernel) |
void |
BudgetedPassiveAggressiveClassification.setKernel(Kernel kernel) |
Constructor and Description |
---|
BudgetedPassiveAggressiveClassification(int budget,
Kernel kernel,
float c,
boolean fairness,
BudgetedPassiveAggressiveClassification.DeletingPolicy deletingPolicy,
Label label) |
BudgetedPassiveAggressiveClassification(int budget,
Kernel kernel,
float cp,
float cn,
BudgetedPassiveAggressiveClassification.DeletingPolicy deletingPolicy,
Label label) |
KernelizedPassiveAggressiveClassification(float cp,
float cn,
PassiveAggressiveClassification.Loss loss,
Kernel kernel,
Label label) |
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelizedPerceptron.getKernel() |
Modifier and Type | Method and Description |
---|---|
void |
KernelizedPerceptron.setKernel(Kernel kernel) |
Constructor and Description |
---|
KernelizedPerceptron(float alpha,
float margin,
boolean unbiased,
Kernel kernel,
Label label) |
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelBasedKMeansEngine.getKernel() |
Modifier and Type | Method and Description |
---|---|
void |
KernelBasedKMeansEngine.setKernel(Kernel kernel) |
Constructor and Description |
---|
KernelBasedKMeansEngine(Kernel kernel,
int k,
int maxIterations) |
Modifier and Type | Method and Description |
---|---|
void |
EpsilonSvmRegression.setKernel(Kernel kernel) |
Constructor and Description |
---|
EpsilonSvmRegression(Kernel kernel,
Label label,
float c,
float pReg) |
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelizedPassiveAggressiveRegression.getKernel() |
Modifier and Type | Method and Description |
---|---|
void |
KernelizedPassiveAggressiveRegression.setKernel(Kernel kernel) |
Constructor and Description |
---|
KernelizedPassiveAggressiveRegression(float aggressiveness,
float epsilon,
PassiveAggressive.Policy policy,
Kernel kernel,
Label label) |
Modifier and Type | Method and Description |
---|---|
Kernel |
KernelMachineModel.getKernel() |
Kernel |
BinaryKernelMachineModel.getKernel() |
Modifier and Type | Method and Description |
---|---|
void |
KernelMachineModel.setKernel(Kernel kernel) |
void |
BinaryKernelMachineModel.setKernel(Kernel kernel) |
Constructor and Description |
---|
BinaryKernelMachineModel(Kernel kernel) |
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