All Classes and Interfaces
Class
Description
A collection of helper methods for performing training and inference in a CRF.
Belief Propagation results.
Clique scores within a chain.
Viterbi output from a linear chain.
Chunk class used for chunk level confidence prediction in the
CRFModel.An inference time model for a linear chain CRF trained using SGD.
The type of subsequence level confidence to predict.
CLI options for training a linear chain CRF model.
A
Parameters for training a CRF using SGD.A trainer for CRFs using SGD.
The inference time version of a factorization machine trained using SGD.
CLI options for training a factorization machine classifier.
Available loss types.
A trainer for a classification factorization machine using SGD.
Hinge loss, scores the correct value margin and any incorrect predictions -margin.
The inference time version of a kernel model trained using Pegasos.
Options for using the KernelSVMTrainer.
The kernel types.
A trainer for a kernelised model using the Pegasos optimiser.
An interface for single label prediction objectives.
The inference time version of a linear model trained using SGD.
CLI options for training a linear classifier.
Available loss types.
A trainer for a linear classifier using SGD.
A logistic regression trainer that uses a reasonable objective, optimiser,
number of epochs and minibatch size.
A multiclass version of the log loss.
Build and run a sequence classifier on a generated dataset.
Command line options.
Build and run a classifier for a standard dataset using FMClassificationTrainer.
Build and run a kernel SVM classifier for a standard dataset.
Build and run a classifier for a standard dataset using LinearSGDTrainer.
Command line options.
Command line options.
Command line options.
SGD utilities.
A nominal tuple.
A nominal tuple.