Uses of Interface
gov.sandia.cognition.algorithm.MeasurablePerformanceAlgorithm

Packages that use MeasurablePerformanceAlgorithm
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.genetic Provides a genetic algorithm implementation. 
gov.sandia.cognition.learning.algorithm.hmm Provides hidden Markov model (HMM) algorithms. 
gov.sandia.cognition.learning.algorithm.pca Provides implementations of Principle Components Analysis (PCA). 
gov.sandia.cognition.learning.algorithm.perceptron Provides the Perceptron algorithm and some of its variations. 
gov.sandia.cognition.learning.algorithm.perceptron.kernel   
gov.sandia.cognition.learning.algorithm.regression Provides regression algorithms, such as Linear Regression. 
gov.sandia.cognition.learning.algorithm.root Provides algorithms for finding the roots, or zero crossings, of scalar functions. 
gov.sandia.cognition.learning.algorithm.svm Provides implementations of Support Vector Machine (SVM) 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. 
gov.sandia.cognition.statistics.distribution Provides statistical distributions. 
gov.sandia.cognition.statistics.method Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods. 
 

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

Classes in gov.sandia.cognition.learning.algorithm.annealing that implement MeasurablePerformanceAlgorithm
 class SimulatedAnnealer<CostParametersType,AnnealedType>
          The SimulatedAnnealer class implements the simulated annealing algorithm using the provided cost function and perturbation function.
 

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

Classes in gov.sandia.cognition.learning.algorithm.clustering that implement MeasurablePerformanceAlgorithm
 class AffinityPropagation<DataType>
          The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
 class DirichletProcessClustering
          Clustering algorithm that wraps Dirichlet Process Mixture Model.
 class KMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
 class KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>>
          Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.
 class OptimizedKMeansClusterer<DataType>
          This class implements an optimized version of the k-means algorithm that makes use of the triangle inequality to compute the same answer as k-means while using less distance calculations.
 class ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          This is a parallel implementation of the k-means clustering algorithm.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.genetic
 

Classes in gov.sandia.cognition.learning.algorithm.genetic that implement MeasurablePerformanceAlgorithm
 class GeneticAlgorithm<CostParametersType,GenomeType>
          The GeneticAlgorithm class implements a generic genetic algorithm that uses a given cost function to minimize and a given reproduction function for generating the population.
 class ParallelizedGeneticAlgorithm<CostParametersType,GenomeType>
          This is a parallel implementation of the genetic algorithm.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.hmm
 

Classes in gov.sandia.cognition.learning.algorithm.hmm that implement MeasurablePerformanceAlgorithm
 class AbstractBaumWelchAlgorithm<ObservationType,DataType>
          Partial implementation of the Baum-Welch algorithm.
 class BaumWelchAlgorithm<ObservationType>
          Implements the Baum-Welch algorithm, also known as the "forward-backward algorithm", the expectation-maximization algorithm, etc for Hidden Markov Models (HMMs).
 class ParallelBaumWelchAlgorithm<ObservationType>
          A Parallelized implementation of some of the methods of the Baum-Welch Algorithm.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.pca
 

Classes in gov.sandia.cognition.learning.algorithm.pca that implement MeasurablePerformanceAlgorithm
 class GeneralizedHebbianAlgorithm
          Implementation of the Generalized Hebbian Algorithm, also known as Sanger's Rule, which is a generalization of Oja's Rule.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.perceptron
 

Classes in gov.sandia.cognition.learning.algorithm.perceptron that implement MeasurablePerformanceAlgorithm
 class BatchMultiPerceptron<CategoryType>
          Implements a multi-class version of the standard batch Perceptron learning algorithm.
 class Perceptron
          The Perceptron class implements the standard Perceptron learning algorithm that learns a binary classifier based on vector input.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.perceptron.kernel
 

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement MeasurablePerformanceAlgorithm
 class KernelAdatron<InputType>
          The KernelAdatron class implements an online version of the Support Vector Machine learning algorithm.
 class KernelPerceptron<InputType>
          The KernelPerceptron class implements the kernel version of the Perceptron algorithm.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.regression
 

Subinterfaces of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.regression
 interface ParameterCostMinimizer<ResultType extends VectorizableVectorFunction>
          A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.
 

Classes in gov.sandia.cognition.learning.algorithm.regression that implement MeasurablePerformanceAlgorithm
 class AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends Double>>
          Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
 class AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>>
          Partial implementation of ParameterCostMinimizer.
 class FletcherXuHybridEstimation
          The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares parameters.
 class GaussNewtonAlgorithm
          Implementation of the Gauss-Newton parameter-estimation procedure.
 class KernelBasedIterativeRegression<InputType>
          The KernelBasedIterativeRegression class implements an online version of the Support Vector Regression algorithm.
 class LeastSquaresEstimator
          Abstract implementation of iterative least-squares estimators.
 class LevenbergMarquardtEstimation
          Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
 class ParameterDerivativeFreeCostMinimizer
          Implementation of a class of objects that uses a derivative-free minimization algorithm.
 class ParameterDifferentiableCostMinimizer
          This class adapts the unconstrained nonlinear minimization algorithms in the "minimization" package to the task of estimating locally optimal (minimum-cost) parameter sets.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.root
 

Classes in gov.sandia.cognition.learning.algorithm.root that implement MeasurablePerformanceAlgorithm
 class AbstractBracketedRootFinder
          Partial implementation of RootFinder that maintains a bracket on the root.
 class AbstractRootFinder
          Partial implementation of RootFinder.
 class RootFinderBisectionMethod
          Bisection algorithm for root finding.
 class RootFinderFalsePositionMethod
          The false-position algorithm for root finding.
 class RootFinderNewtonsMethod
          Newton's method, sometimes called Newton-Raphson method, uses first-order derivative information to iteratively locate a root.
 class RootFinderRiddersMethod
          The root-finding algorithm due to Ridders.
 class RootFinderSecantMethod
          The secant algorithm for root finding.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.learning.algorithm.svm
 

Classes in gov.sandia.cognition.learning.algorithm.svm that implement MeasurablePerformanceAlgorithm
 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.
 class SuccessiveOverrelaxation<InputType>
          The SuccessiveOverrelaxation class implements the Successive Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM).
 

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

Classes in gov.sandia.cognition.learning.function.vector that implement MeasurablePerformanceAlgorithm
static class GaussianContextRecognizer.Learner
          Creates a GaussianContextRecognizer from a Dataset using a BatchClusterer
 

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

Classes in gov.sandia.cognition.statistics.bayesian that implement MeasurablePerformanceAlgorithm
 class MetropolisHastingsAlgorithm<ObservationType,ParameterType>
          An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.statistics.distribution
 

Classes in gov.sandia.cognition.statistics.distribution that implement MeasurablePerformanceAlgorithm
static class MixtureOfGaussians.EMLearner
          An Expectation-Maximization based "soft" assignment learner.
static class MixtureOfGaussians.Learner
          A hard-assignment learner for a MixtureOfGaussians
static class ScalarMixtureDensityModel.EMLearner
          An EM learner that estimates a mixture model from data
 

Uses of MeasurablePerformanceAlgorithm in gov.sandia.cognition.statistics.method
 

Classes in gov.sandia.cognition.statistics.method that implement MeasurablePerformanceAlgorithm
 class DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>>
          A method of estimating the parameters of a distribution using an arbitrary CostFunction and FunctionMinimizer algorithm.