Improved Ensemble Training for Hidden Markov Models using Random Relative Node Permutations

Relative Entropy Rate between a Markov Chain and Its Corresponding Hidden Markov Chain

 In this paper we study the relative entropy rate between a homogeneous Markov chain and a hidden Markov chain defined by observing the output of a discrete stochastic channel whose input is the finite state space homogeneous stationary Markov chain. For this purpose, we obtain the relative entropy between two finite subsequences of above mentioned chains with the help of the definition of...

Ensemble Learning for Hidden Markov Models

The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-tting, and contains no indication of parameter uncertainty. Also, this maximummay be unrepresentative of the posterior probability distribution. In this...

Introducing Busy Customer Portfolio Using Hidden Markov Model

Due to the effective role of Markov models in customer relationship management (CRM), there is a lack of comprehensive literature review which contains all related literatures. In this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. One hundred articles were identified and reviewed to find direct relevance for applying Markov models...

Incremental Boolean Combination of Classifiers

The incremental Boolean combination (incrBC ) technique is a new learn-and-combine approach that is proposed to adapt ensemblebased pattern classification systems over time, in response to new data acquired during operations. When a new block of training data becomes available, this technique generates a diversified pool of base classifiers from the data by varying training hyperparameters and ...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words