Learning Geometrically - Constrained Hidden Markov Models forRobot

Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap

Hidden Markov models (hmms) and partially observable Markov decision processes (pomdps) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and o ce buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily availab...

Learning performance of Tikhonov regularization algorithm with geometrically beta-mixing observations

Estimating the generalization performance of learning algorithms is one of the main purposes of machine learning theoretical research. The previous results describing the generalization ability of Tikhonov regularization algorithm are almost all based on independent and identically distributed (i.i.d.) samples. In this paper we go far beyond this classical framework by establishing the bound on...

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...

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

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