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## Uses of Class
fr.lip6.kernel.Kernel

Packages that use Kernel
fr.lip6.classifier |
fr.lip6.density |
fr.lip6.kernel | Package containing generic kernels on vectors.
fr.lip6.kernel.adaptative |
fr.lip6.kernel.extra |
fr.lip6.kernel.extra.bag |
fr.lip6.kernel.extra.colt |
fr.lip6.kernel.typed |
fr.lip6.kernel.typed.index |

Uses of Kernel in fr.lip6.classifier

Methods in fr.lip6.classifier with parameters of type Kernel
`void` | `SimpleMKL.addKernel(Kernel<T> k)`
`void` | `SMOSVM.setKernel(Kernel<T> k)`

Constructors in fr.lip6.classifier with parameters of type Kernel
`EnhancedSMOSVM(Kernel<T> k)` Constructeur passant le noyau servant à calculer la similarité entre les éléments de l'espace d'entrée.
`ParzenClassifier(Kernel<T> kernel)`
`PegasosSVM(Kernel<T> k)`
`SMOSVM(Kernel<T> k)` Constructeur passant le noyau servant à calculer la similarité entre les éléments de l'espace d'entrée.

Uses of Kernel in fr.lip6.density

Constructors in fr.lip6.density with parameters of type Kernel
`ParzenDensity(Kernel<T> kernel)`
`SMODensity(Kernel<T> K)` Constructeur par défaut, fournissant le noyau servant à évaluer la similarité entre les élement de l'espace d'entrée.

Uses of Kernel in fr.lip6.kernel

Subclasses of Kernel in fr.lip6.kernel
`class` | `CMKernel<T>` Generic kernel with a matrix based cache policy.
`class` | `ThreadedKernel<T>` Simple multithreaded implementation over a given Kernel.

Fields in fr.lip6.kernel declared as Kernel
`protected Kernel<T>` | `ThreadedKernel.k`

Constructors in fr.lip6.kernel with parameters of type Kernel
`CMKernel(Kernel<T> k)` Default constructor
`ThreadedKernel(Kernel<T> kernel)` MultiThread the given kernel

Uses of Kernel in fr.lip6.kernel.adaptative

Subclasses of Kernel in fr.lip6.kernel.adaptative
`class` | `ThreadedSumKernel<T>` Major kernel computed as a weighted sum of minor kernels : K = w_i * k_i Computation of the kernel matrix is done by running a thread on sub matrices.
`class` | `WeightedSumKernel<T>` Major kernel computed as a weighted sum of minor kernels : K = w_i * k_i

Methods in fr.lip6.kernel.adaptative that return types with arguments of type Kernel
`java.util.Hashtable<Kernel<T>,java.lang.Double>` | `WeightedSumKernel.getWeights()` get the list of kernels and associated weights.
`java.util.Hashtable<Kernel<T>,java.lang.Double>` | `ThreadedSumKernel.getWeights()` get the list of kernels and associated weights.

Methods in fr.lip6.kernel.adaptative with parameters of type Kernel
`void` | `WeightedSumKernel.addKernel(Kernel<T> k)` adds a kernel to the sum with weight 1.0
`void` | `ThreadedSumKernel.addKernel(Kernel<T> k)` adds a kernel to the sum with weight 1.0
`void` | `WeightedSumKernel.addKernel(Kernel<T> k, double d)` adds a kernel to the sum with weight d
`void` | `ThreadedSumKernel.addKernel(Kernel<T> k, double d)` adds a kernel to the sum with weight d
`double` | `WeightedSumKernel.getWeight(Kernel<T> k)` gets the weights of kernel k
`double` | `ThreadedSumKernel.getWeight(Kernel<T> k)` gets the weights of kernel k
`void` | `WeightedSumKernel.removeKernel(Kernel<T> k)` removes kernel k from the sum
`void` | `ThreadedSumKernel.removeKernel(Kernel<T> k)` removes kernel k from the sum
`void` | `WeightedSumKernel.setWeight(Kernel<T> k, java.lang.Double d)` Sets the weight of kernel k
`void` | `ThreadedSumKernel.setWeight(Kernel<T> k, java.lang.Double d)` Sets the weight of kernel k

Constructor parameters in fr.lip6.kernel.adaptative with type arguments of type Kernel
`ThreadedSumKernel(java.util.Hashtable<Kernel<T>,java.lang.Double> h)` Sets the weights to h.
`WeightedSumKernel(java.util.Hashtable<Kernel<T>,java.lang.Double> h)` Sets the weights to h.

Uses of Kernel in fr.lip6.kernel.extra

Subclasses of Kernel in fr.lip6.kernel.extra
`class` | `PowerKernel<T>`

Constructors in fr.lip6.kernel.extra with parameters of type Kernel
`PowerKernel(Kernel<T> kernel, double e)`

Uses of Kernel in fr.lip6.kernel.extra.bag

Subclasses of Kernel in fr.lip6.kernel.extra.bag
`class` | `PowerBagKernel<S,T extends ArrayList<S>>`
`class` | `SimpleSubListKernel<S,T extends ArrayList<S>>` Kernel on bags of same length.
`class` | `SubListKernel<S,T extends ArrayList<S>>` Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
`class` | `SubListMaxKernel<S,T extends ArrayList<S>>` max value of kernel between to bags
`class` | `SubListPowerKernel<S,T extends ArrayList<S>>`

Constructors in fr.lip6.kernel.extra.bag with parameters of type Kernel
`PowerBagKernel(double exp, Kernel<S> kernel)`
`SimpleSubListKernel(int from, int to, Kernel<S> kernel)`
`SubListKernel(int from, int to, Kernel<S> kernel)`
`SubListMaxKernel(int from, int to, Kernel<S> kernel)`
`SubListPowerKernel(int from, int to, Kernel<S> kernel, double e)`

Uses of Kernel in fr.lip6.kernel.extra.colt

Subclasses of Kernel in fr.lip6.kernel.extra.colt
`class` | `GeneralizedSparseDoubleLinear`
`class` | `SparseDoubleGaussChi2`
`class` | `SparseDoubleLinear`
`class` | `SparseGeneralizedSparseDoubleLinear`

Uses of Kernel in fr.lip6.kernel.typed

Subclasses of Kernel in fr.lip6.kernel.typed
`class` | `DoubleGaussChi1` Gaussian Kernel on double[] that uses a Chi1 distance.
`class` | `DoubleGaussChi2` Gaussian Kernel on double[] that uses a Chi2 distance.
`class` | `DoubleGaussL2` Gaussian Kernel on double[] that uses a L2 distance.
`class` | `DoubleLinear` Linear Kernel on double[].
`class` | `FloatGaussChi2` Gaussian Kernel on double[] that uses a Chi2 distance.
`class` | `GeneralizedDoubleLinear`
`class` | `IntGaussChi1` Gaussian Kernel on int[] that uses a Chi1 distance.
`class` | `IntGaussChi2` Gaussian Kernel on int[] that uses a Chi2 distance.
`class` | `IntGaussL2` Gaussian Kernel on int[] that uses a L2 distance.
`class` | `SPDoubleGaussChi2` Gaussian Kernel on double[] that uses a Chi2 distance.

Uses of Kernel in fr.lip6.kernel.typed.index

Subclasses of Kernel in fr.lip6.kernel.typed.index
`class` | `IndexDoubleGaussL2` Kernel on double[] that computes the L2 distance of a specified component j: k(x, y) = (x[j]-y[j])*(x[j]-y[j])
`class` | `IndexDoubleLinear` Kernel on double[] that performs the product of a specified component j: k(x,y) = x[j]*y[j]

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