In this paper, the research and demonstration work on the issues is as follows : First section is about determining number of hidden states in system call sequence modeling process. 本文就这些问题进行了以下一些研究和论证的工作:一是关于在系统调用序列建模过程中隐马尔科夫模型的隐含状态数的确定问题。
The number of hidden states is a very important parameter in the training process of hidden Markov model, which directly affects the accuracy of the model. 隐含状态数是隐马尔科夫模型训练过程中一个很重要的参数,直接影响到模型的精确性。
Hidden Markov models have proven to be one of the most widely used tools for learning probabilistic models of dynamics time series. HMM can model dynamical behavior variation existing in the system through a latent variable ( hidden states ). 隐Markov模型(HMM)已经证明是学习动态时间序列的概率模型的最广泛应用的工具之一,它可以使用一个隐变量来模拟系统的动态行为的变化。
Then the online EM algorithm is also given when the assumption of hidden states changes into second-order homogeneous Markov chain. 另外给出了当隐过程是二阶时间齐次马尔可夫链时的可更新的EM算法。
Hidden Markov Model ( HMM ) is a statistical model which assumes the observa-tion sequence is generated by a Markov process with hidden states. 隐马尔可夫模型(HiddenMarkovmodel,HMM)是一种概率模型,它假定观测序列是由包含若干隐状态的马尔可夫过程产生的。