隐藏的马尔可夫模型程序 [英] HIDDEN MARKOV MODEL PROGRAM

问题描述

我需要有关如何创建机器学习算法的帮助.
将有一个包含许多观察序列的文件，例如;

0-1-0-1-0-0-0-0

程序读取类似于示例序列的数千行，并从用户那里获取模型中的状态数.通过使用此信息，它将创建一个模型，其中包括状态转换，初始状态和观察结果出现的概率.我应该使用鲍姆·韦尔奇(Baum Welch)方法.

Hi,

I need help about how creating a machine learning algoritm.
There''ll be a file contains many observation sequences such as;

0-1-0-1-0-0-0-0

Program reads thousands of lines similar to example sequence and takes the number of states in the model from user. By using this information, it will create model which includes states transition,initial state and observations appear probabilites. And I should use Baum Welch method for it.
Are there anyone to help me about it?

推荐答案

只是为了使事情正确:
HMM算法 [ JHAMM [

Just to get things right:
the HMM algorithm[^] aka Baum-Welch Model

Seems like there is a Java-lib available: JHAMM[^]

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