Uses of Class

Packages that use AbstractRandomized
gov.sandia.cognition.learning.algorithm.annealing Provides the Simulated Annealing algorithm. 
gov.sandia.cognition.learning.algorithm.clustering Provides clustering algorithms. 
gov.sandia.cognition.learning.algorithm.clustering.initializer Provides implementations of methods for selecting initial clusters. 
gov.sandia.cognition.learning.algorithm.genetic.reproducer Provides reproduction functions for use with a Genetic Algorithm. 
gov.sandia.cognition.learning.algorithm.tree Provides decision tree learning algorithms. Provides data set utilities for learning. Provides data feature extractors. 
gov.sandia.cognition.learning.experiment Provides experiments for validating the performance of learning algorithms. 
gov.sandia.cognition.learning.function.vector Provides functions that output vectors. 
gov.sandia.cognition.statistics.bayesian Provides algorithms for computing Bayesian estimates of parameters. 

Uses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.annealing

Subclasses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.annealing
 class VectorizablePerturber
          The VectorizablePerturber implements a Perturber for Vectorizable objects.

Uses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.clustering

Subclasses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.clustering
 class KMeansFactory
          Creates a parallelized version of the k-means clustering algorithm for the typical use: clustering vector data with a Euclidean distance metric.

Uses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.clustering.initializer

Subclasses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.clustering.initializer
 class NeighborhoodGaussianClusterInitializer
          Creates GaussianClusters near existing, but not on top of, data points.

Uses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.genetic.reproducer

Subclasses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.genetic.reproducer
 class VectorizableCrossoverFunction
          The VectorizableCrossoverFunction class is a CrossoverFunction that takes two Vectorizable.

Uses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.tree

Subclasses of AbstractRandomized in gov.sandia.cognition.learning.algorithm.tree
 class RandomSubVectorThresholdLearner<OutputType>
          Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner.

Uses of AbstractRandomized in

Subclasses of AbstractRandomized in
 class RandomDataPartitioner<DataType>
          The RandomDataPartitioner class implements a randomized data partitioner that takes a collection of data and randomly splits it into training and testing sets based on a fixed percentage of training data.

Uses of AbstractRandomized in

Subclasses of AbstractRandomized in
 class RandomSubspace
          Selects a random subspace from the given vector, which is a random set of indices.

Uses of AbstractRandomized in gov.sandia.cognition.learning.experiment

Subclasses of AbstractRandomized in gov.sandia.cognition.learning.experiment
 class CrossFoldCreator<DataType>
          The CrossFoldCreator implements a validation fold creator that creates folds for a typical k-fold cross-validation experiment.
 class RandomByTwoFoldCreator<DataType>
          A validation fold creator that takes a given collection of data and randomly splits it in half a given number of times, returning two folds for each split, using one half as training and the other half as testing.

Uses of AbstractRandomized in gov.sandia.cognition.learning.function.vector

Subclasses of AbstractRandomized in gov.sandia.cognition.learning.function.vector
 class ThreeLayerFeedforwardNeuralNetwork
          This is a "standard" feedforward neural network with a single hidden layer.

Uses of AbstractRandomized in gov.sandia.cognition.statistics.bayesian

Subclasses of AbstractRandomized in gov.sandia.cognition.statistics.bayesian
 class ImportanceSampling<ObservationType,ParameterType>
          Importance sampling is a Monte Carlo inference technique where we sample from an easy distribution over the hidden variables (parameters) and then weight the result by the ratio of the likelihood of the parameters given the evidence and the likelihood of generating the parameters.
 class RejectionSampling<ObservationType,ParameterType>
          Rejection sampling is a method of inferring hidden parameters by using an easy-to-sample-from distribution (times a scale factor) that envelopes another distribution that is difficult to sample from.