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为什么我们在培训“隐藏的Markov模式”时会犹豫不决
原标题:Why do we iterate when training a Hidden Markov Model

I m using a hidden markov model for classification, the jahmm implementation.

在培训一个模型一时,使用先式模型组群。 然后是使用任意的循环,优化模型。 我很想知道,这些灾难发生。

我的争.告诉我, se是建立在最初模式基础上的,而这种模式又被用于再培训。

is this true or is there something else which happens?

谢谢!

问题回答

BaumWelch Learninger.java:

public <O extends Observation> Hmm<O>
    learn(Hmm<O> initialHmm, List<? extends List<? extends O>> sequences)
    {
        Hmm<O> hmm = initialHmm;

        for (int i = 0; i < nbIterations; i++)
            hmm = iterate(hmm, sequences);

        return hmm;
    }

实际上,它正在每艘航程中再一次使用规定的观察顺序。 由于模型有时会慢慢到地方的顶峰,因此需要变形。 a. 起草一个类似方案,在每一版本之后看到模型:

BaumWelchLearner bwl = new BaumWelchLearner();
for (int i=0; i<=bwl.getNbIterations(); i++) {
    Hmm iteration = bwl.iterate(yourHmm, learningSequences);
    System.out.println("
Iteration " + i + ":
" + iteration.toString());
    yourHmm = iteration;
}




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