C# 클래스 Accord.Statistics.Models.Markov.Learning.MaximumLikelihoodLearning

Maximum Likelihood learning algorithm for discrete-density Hidden Markov Models.

The maximum likelihood estimate is a supervised learning algorithm. It considers both the sequence of observations as well as the sequence of states in the Markov model are visible and thus during training.

Often, the Maximum Likelihood Estimate can be used to give a starting point to a unsupervised algorithm, making possible to use semi-supervised techniques with HMMs. It is possible, for example, to use MLE to guess initial values for an HMM given a small set of manually labeled labels, and then further estimate this model using the Viterbi learning algorithm.

상속: ISupervisedLearning

파일 보기 프로젝트 열기: accord-net/framework 1 사용 예제들

공개 메소드들

메소드	설명
MaximumLikelihoodLearning ( HiddenMarkovModel model ) : System	Creates a new instance of the Maximum Likelihood learning algorithm.
Run ( int observations, int paths ) : double	Runs the Maximum Likelihood learning algorithm for hidden Markov models. Supervised learning problem. Given some training observation sequences O = {o1, o2, ..., oK}, known training state paths H = {h1, h2, ..., hK} and general structure of HMM (numbers of hidden and visible states), determine HMM parameters M = (A, B, pi) that best fit training data.

비공개 메소드들

메소드	설명
ISupervisedLearning ( Array observations, int paths ) : double	Runs the Maximum Likelihood learning algorithm for hidden Markov models. Supervised learning problem. Given some training observation sequences O = {o1, o2, ..., oK}, known training state paths H = {h1, h2, ..., hK} and general structure of HMM (numbers of hidden and visible states), determine HMM parameters M = (A, B, pi) that best fit training data.

메소드 상세

MaximumLikelihoodLearning() 공개 메소드

Creates a new instance of the Maximum Likelihood learning algorithm.

public MaximumLikelihoodLearning ( HiddenMarkovModel model ) : System
model	HiddenMarkovModel
리턴	System

Run() 공개 메소드

Runs the Maximum Likelihood learning algorithm for hidden Markov models.

Supervised learning problem. Given some training observation sequences O = {o1, o2, ..., oK}, known training state paths H = {h1, h2, ..., hK} and general structure of HMM (numbers of hidden and visible states), determine HMM parameters M = (A, B, pi) that best fit training data.

public Run ( int observations, int paths ) : double
observations	int	An array of observation sequences to be used to train the model.
paths	int	An array of state labels associated to each observation sequence.
리턴	double