Package | Description |
---|---|
gov.sandia.cognition.learning.algorithm.pca |
Provides implementations of Principle Components Analysis (PCA).
|
gov.sandia.cognition.learning.algorithm.perceptron.kernel | |
gov.sandia.cognition.learning.algorithm.svm |
Provides implementations of Support Vector Machine (SVM) learning algorithms.
|
gov.sandia.cognition.learning.function.categorization |
Provides functions that output a discrete set of categories.
|
gov.sandia.cognition.learning.function.kernel |
Provides kernel functions.
|
gov.sandia.cognition.learning.function.scalar |
Provides functions that output real numbers.
|
gov.sandia.cognition.statistics.bayesian |
Provides algorithms for computing Bayesian estimates of parameters.
|
Modifier and Type | Class and Description |
---|---|
class |
KernelPrincipalComponentsAnalysis<DataType>
An implementation of the Kernel Principal Components Analysis (KPCA)
algorithm.
|
static class |
KernelPrincipalComponentsAnalysis.Function<DataType>
The resulting transformation function learned by Kernel Principal
Components Analysis.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractOnlineBudgetedKernelBinaryCategorizerLearner<InputType>
An abstract implementation of the
BudgetedKernelBinaryCategorizerLearner
for online learners. |
class |
AbstractOnlineKernelBinaryCategorizerLearner<InputType>
An abstract class for an online kernel binary categorizer learner.
|
class |
Forgetron<InputType>
An implementation of the "self-tuned" Forgetron algorithm, which is an online
budgeted kernel binary categorizer learner.
|
static class |
Forgetron.Basic<InputType>
An implementation of the "basic" Forgetron algorithm, which is an online
budgeted kernel binary categorizer learner.
|
static class |
Forgetron.Greedy<InputType>
An implementation of the "greedy" Forgetron algorithm, which is an online
budgeted kernel binary categorizer learner.
|
static class |
Forgetron.Result<InputType>
The result object learned by the
Forgetron , which extends
the DefaultKernelBinaryCategorizer with some additional state
information needed in the update step. |
class |
KernelBinaryCategorizerOnlineLearnerAdapter<InputType>
A wrapper class for a
KernelizableBinaryCategorizerOnlineLearner
that allows it to be used as a batch or incremental learner over the
input type directly, rather than using utility methods. |
class |
OnlineKernelPerceptron<InputType>
An implementation of the online version of the Perceptron algorithm.
|
class |
OnlineKernelRandomizedBudgetPerceptron<InputType>
An implementation of a fixed-memory kernel Perceptron algorithm.
|
class |
Projectron<InputType>
An implementation of the Projectron algorithm, which is an online kernel
binary categorizer learner that has a budget parameter tuned by the eta
parameter.
|
static class |
Projectron.LinearSoftMargin<InputType>
An implementation of the Projectron++ algorithm, which is an online
kernel binary categorizer learner that has a budget parameter tuned by
the eta parameter.
|
class |
RemoveOldestKernelPerceptron<InputType>
A budget kernel Perceptron that always removes the oldest item.
|
class |
Stoptron<InputType>
An online, budgeted, kernel version of the Perceptron algorithm that stops
learning once it has reached its budget.
|
Modifier and Type | Class and Description |
---|---|
class |
SequentialMinimalOptimization<InputType>
An implementation of the Sequential Minimal Optimization (SMO) algorithm for
training a Support Vector Machine (SVM), which is a kernel-based binary
categorizer.
|
Modifier and Type | Class and Description |
---|---|
class |
DefaultKernelBinaryCategorizer<InputType>
A default implementation of the
KernelBinaryCategorizer that uses
the standard way of representing the examples (supports) using a
DefaultWeightedValue . |
class |
KernelBinaryCategorizer<InputType,EntryType extends WeightedValue<? extends InputType>>
The
KernelBinaryCategorizer class implements a binary
categorizer that uses a kernel to do its categorization. |
Modifier and Type | Class and Description |
---|---|
class |
DefaultKernelContainer<InputType>
The
DefaultKernelContainer class implements an object that
contains a kernel inside. |
class |
ExponentialKernel<InputType>
The
ExponentialKernel class implements a kernel that applies the
exponential function to the result of another kernel. |
class |
KernelDistanceMetric<InputType>
The
KernelDistanceMetric class implements a distance metric that
utilizes an underlying Kernel for computing the distance. |
class |
NormalizedKernel<InputType>
The
NormalizedKernel class implements an Kernel
that returns a normalized value between 0.0 and 1.0 by normalizing the
results of a given kernel. |
class |
VectorFunctionKernel
The
VectorFunctionKernel implements a kernel that makes use of a
vector function plus a kernel that operates on vectors. |
class |
WeightedKernel<InputType>
The
WeightedKernel class implements a kernel that takes another
kernel, evaluates it, and then the result is rescaled by a given weight. |
Modifier and Type | Class and Description |
---|---|
class |
KernelScalarFunction<InputType>
The
KernelScalarFunction class implements a scalar function that
uses a kernel to compute its output value. |
class |
LocallyWeightedKernelScalarFunction<InputType>
The
LocallyWeightedKernelScalarFunction class implements a scalar
function that uses kernels and does local weighting on them to get the
result value. |
Modifier and Type | Class and Description |
---|---|
class |
GaussianProcessRegression<InputType>
Gaussian Process Regression, is also known as Kriging, is a nonparametric
method to interpolate and extrapolate using Bayesian regression, where
the expressiveness of the estimator can grow with the data.
|