Uses of Interface
jsat.classifiers.calibration.BinaryScoreClassifier

Packages that use BinaryScoreClassifier

Package	Description
jsat.classifiers
jsat.classifiers.boosting
jsat.classifiers.calibration
jsat.classifiers.linear
jsat.classifiers.linear.kernelized
jsat.classifiers.neuralnetwork
jsat.classifiers.svm
jsat.classifiers.svm.extended

Uses of BinaryScoreClassifier in jsat.classifiers

Classes in jsat.classifiers that implement BinaryScoreClassifier

Modifier and Type	Class and Description
class	RegressorToClassifier This meta algorithm wraps a Regressor to perform binary classification.

Uses of BinaryScoreClassifier in jsat.classifiers.boosting

Classes in jsat.classifiers.boosting that implement BinaryScoreClassifier

Modifier and Type	Class and Description
class	EmphasisBoost Emphasis Boost is a generalization of the Real AdaBoost algorithm, expanding the update term and providing the λ term to control the trade off.
class	ModestAdaBoost Modest Ada Boost is a generalization of Discrete Ada Boost that attempts to reduce the generalization error and avoid over-fitting.

Uses of BinaryScoreClassifier in jsat.classifiers.calibration

Fields in jsat.classifiers.calibration declared as BinaryScoreClassifier

Modifier and Type	Field and Description
protected BinaryScoreClassifier	BinaryCalibration.base The base classifier to train and calibrate the outputs of

Methods in jsat.classifiers.calibration that return BinaryScoreClassifier

Modifier and Type	Method and Description
BinaryScoreClassifier	BinaryScoreClassifier.clone()

Constructors in jsat.classifiers.calibration with parameters of type BinaryScoreClassifier

Constructor and Description
BinaryCalibration(BinaryScoreClassifier base, BinaryCalibration.CalibrationMode mode) Creates a new Binary Calibration object
IsotonicCalibration(BinaryScoreClassifier base, BinaryCalibration.CalibrationMode mode) Creates a new Isotonic Calibration object
PlattCalibration(BinaryScoreClassifier base, BinaryCalibration.CalibrationMode mode) Creates a new Platt Calibration object

Uses of BinaryScoreClassifier in jsat.classifiers.linear

Classes in jsat.classifiers.linear that implement BinaryScoreClassifier

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	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	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	STGD This provides an implementation of Sparse Truncated Gradient Descent for L1 regularized linear classification and regression on sparse data sets.

Uses of BinaryScoreClassifier in jsat.classifiers.linear.kernelized

Classes in jsat.classifiers.linear.kernelized that implement BinaryScoreClassifier

Modifier and Type	Class and Description
class	ALMA2K Provides a kernelized version of the ALMA2 algorithm.
class	BOGD Bounded Online Gradient Descent (BOGD) is a kernel learning algorithm that uses a bounded number of support vectors.
class	DUOL Provides an implementation of Double Update Online Learning (DUOL) algorithm.
class	Forgetron Implementation of the first two Forgetron algorithms.
class	OSKL Online Sparse Kernel Learning by Sampling and Smooth Losses (OSKL) is an online algorithm for learning sparse kernelized solutions to binary classification problems.
class	Projectron An implementation of the Projectron and Projectrion++ algorithms.

Uses of BinaryScoreClassifier in jsat.classifiers.neuralnetwork

Classes in jsat.classifiers.neuralnetwork that implement BinaryScoreClassifier

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 BinaryScoreClassifier in jsat.classifiers.svm

Classes in jsat.classifiers.svm that implement BinaryScoreClassifier

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	DCSVM This is an implementation of the Divide-and-Conquer Support Vector Machine (DC-SVM).
class	LSSVM The Least Squares Support Vector Machine (LS-SVM) is an alternative to the standard SVM classifier for regression and binary classification problems.
class	Pegasos Implements the linear kernel mini-batch version of the Pegasos SVM classifier.
class	PegasosK Implements the kernelized version of the Pegasos algorithm for SVMs.
class	PlattSMO An implementation of SVMs using Platt's Sequential Minimum Optimization (SMO) for both Classification and Regression problems.
class	SBP Implementation of the Stochastic Batch Perceptron (SBP) algorithm.
class	SVMnoBias This class implements a version of the Support Vector Machine without a bias term.

Uses of BinaryScoreClassifier in jsat.classifiers.svm.extended

Classes in jsat.classifiers.svm.extended that implement BinaryScoreClassifier

Modifier and Type	Class and Description
class	CPM This class implements the Convex Polytope Machine (CPM), which is an extension of the Linear SVM.