Uses of Package
jsat.classifiers.linear

Packages that use jsat.classifiers.linear

Package	Description
jsat.classifiers.linear

Classes in jsat.classifiers.linear used by jsat.classifiers.linear

Class and Description
ALMA2 Provides a linear implementation of the ALMAp algorithm for p = 2, which is considerably more efficient to compute.
AROW An implementation of Adaptive Regularization of Weight Vectors (AROW), which uses second order information to learn a large margin binary classifier.
BBR This is an implementation of Bayesian Binary Regression for L1 and L2 regularized logistic regression.
BBR.Prior Valid priors that control what type of regularization is applied
LinearBatch LinearBatch learns either a classification or regression problem depending on the loss function ℓ(w,x) used.
LinearL1SCD Implements an iterative and single threaded form of fast Stochastic Coordinate Decent for optimizing L1 regularized linear regression problems.
LinearSGD LinearSGD learns either a classification or regression problem depending on the loss function ℓ(w,x) used.
LogisticRegressionDCD This provides an implementation of regularized logistic regression using Dual Coordinate Descent.
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).
NHERD Implementation of the Normal Herd (NHERD) algorithm for learning a linear binary classifier.
NHERD.CovMode Sets what form of covariance matrix to use
PassiveAggressive An implementations of the 3 versions of the Passive Aggressive algorithm for binary classification and regression.
PassiveAggressive.Mode Controls which version of the Passive Aggressive update is used
ROMMA Provides an implementation of the linear Relaxed online Maximum Margin algorithm, which finds a similar solution to SVMs.
SCD Implementation of Stochastic Coordinate Descent for L1 regularized classification and regression.
SCW Provides an Implementation of Confidence-Weighted (CW) learning and Soft Confidence-Weighted (SCW), both of which are binary linear classifiers inspired by PassiveAggressive.
SCW.Mode Which version of the algorithms shuld be used
SMIDAS Implements the iterative and single threaded stochastic solver for L1 regularized linear regression problems SMIDAS (Stochastic Mirror Descent Algorithm mAde Sparse).
SPA Support class Passive Aggressive (SPA) is a multi class generalization of PassiveAggressive.
STGD This provides an implementation of Sparse Truncated Gradient Descent for L1 regularized linear classification and regression on sparse data sets.
StochasticMultinomialLogisticRegression This is a Stochastic implementation of Multinomial Logistic Regression.
StochasticMultinomialLogisticRegression.Prior Represents a prior of the coefficients that can be applied to perform regularization.
StochasticSTLinearL1 This base class provides shared functionality and variables used by two different training algorithms for L1 regularized linear models.
StochasticSTLinearL1.Loss