Uses of Interface
jsat.SingleWeightVectorModel

Packages that use SingleWeightVectorModel

Package	Description
jsat.classifiers.linear
jsat.classifiers.neuralnetwork
jsat.classifiers.svm
jsat.regression

Uses of SingleWeightVectorModel in jsat.classifiers.linear

Classes in jsat.classifiers.linear that implement SingleWeightVectorModel

Modifier and Type	Class and Description
class	ALMA2 Provides a linear implementation of the ALMAp algorithm for p = 2, which is considerably more efficient to compute.
class	AROW An implementation of Adaptive Regularization of Weight Vectors (AROW), which uses second order information to learn a large margin binary classifier.
class	BBR This is an implementation of Bayesian Binary Regression for L1 and L2 regularized logistic regression.
class	LinearL1SCD Implements an iterative and single threaded form of fast Stochastic Coordinate Decent for optimizing L1 regularized linear regression problems.
class	LogisticRegressionDCD This provides an implementation of regularized logistic regression using Dual Coordinate Descent.
class	NewGLMNET NewGLMNET is a batch method for solving Elastic Net regularized Logistic Regression problems of the form 0.5 * (1-α) ||w||2 + α * ||w||1 + C * ∑Ni=1 ℓ (wT xi + b, yi).
class	NHERD Implementation of the Normal Herd (NHERD) algorithm for learning a linear binary classifier.
class	PassiveAggressive An implementations of the 3 versions of the Passive Aggressive algorithm for binary classification and regression.
class	ROMMA Provides an implementation of the linear Relaxed online Maximum Margin algorithm, which finds a similar solution to SVMs.
class	SCD Implementation of Stochastic Coordinate Descent for L1 regularized classification and regression.
class	SCW Provides an Implementation of Confidence-Weighted (CW) learning and Soft Confidence-Weighted (SCW), both of which are binary linear classifiers inspired by PassiveAggressive.
class	SMIDAS Implements the iterative and single threaded stochastic solver for L1 regularized linear regression problems SMIDAS (Stochastic Mirror Descent Algorithm mAde Sparse).
class	STGD This provides an implementation of Sparse Truncated Gradient Descent for L1 regularized linear classification and regression on sparse data sets.
class	StochasticSTLinearL1 This base class provides shared functionality and variables used by two different training algorithms for L1 regularized linear models.

Uses of SingleWeightVectorModel in jsat.classifiers.neuralnetwork

Classes in jsat.classifiers.neuralnetwork that implement SingleWeightVectorModel

Modifier and Type	Class and Description
class	Perceptron The perceptron is a simple algorithm that attempts to find a hyperplane that separates two classes.

Uses of SingleWeightVectorModel in jsat.classifiers.svm

Classes in jsat.classifiers.svm that implement SingleWeightVectorModel

Modifier and Type	Class and Description
class	DCD Implements Dual Coordinate Descent (DCD) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification and regression.
class	DCDs Implements Dual Coordinate Descent with shrinking (DCDs) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification and regression.
class	Pegasos Implements the linear kernel mini-batch version of the Pegasos SVM classifier.

Uses of SingleWeightVectorModel in jsat.regression

Classes in jsat.regression that implement SingleWeightVectorModel

Modifier and Type	Class and Description
class	LogisticRegression Logistic regression is a common method used to fit a probability between binary outputs.
class	MultipleLinearRegression
class	StochasticRidgeRegression A Stochastic implementation of Ridge Regression.