The Baum-Welch (EM) algorithm is a familiar tool for calculation the maximum likelihood estimate of the parameters in hidden Markov chain models. For the particular case of a binary Markov chain corrupted by binary channel noise a detailed study is carried out of the innuence that the initial conditions impose on the results produced by the algorithm.

We present a polynomial time algorithm to learn an intersection of a constant number of halfspaces in n dimensions over the uniform distribution on an n dimensional ball The al gorithm we present in fact can learn an intersection of an arbitrary polynomial number of halfspaces over this distribution if the subspace spanned by the normal vectors to the bounding hyperplanes has constant dimension...

We describe an extension to the Baum-Welch algorithm for training Hidden Markov Models that uses explicit phoneme segmentation to constrain the forward and backward lattice. The HMMs trained with this algorithm can be shown to improve the accuracy of automatic phoneme segmentation. In addition, this algorithm is significantly more computationally efficient than the full BaumWelch algorithm, whi...

We present an algorithm to compute a full set of irreducible representations of a supersolvable group G over a finite field K, charK |G|, which is not assumed to be a splitting field of G. The main subroutines of our algorithm are a modification of the algorithm of Baum and Clausen (Math. Comp. 63 (1994), 351–359) to obtain information on algebraically conjugate representations, and an effectiv...

Hidden Markov models have been used to restore recorded signals of single ion channels buried in background noise. Parameter estimation and signal restoration are usually carried out through likelihood maximization by using variants of the Baum-Welch forward-backward procedures. This paper presents an alternative approach for dealing with this inferential task. The inferences are made by using ...

The success of many real-world applications demonstrates that hidden Markov models (HMMs) are highly effective in one-dimensional pattern recognition problems such as speech recognition. Research is now focussed on extending HMMs to 2-D and possibly 3-D applications which arise in gesture, face, and handwriting recognition. Although the HMM has become a major workhorse of the pattern recognitio...

This paper reports the results of a system that performs network anomaly detection through the use of Hidden Markov Models (HMMs). The HMMs used to detect anomalies are designed and trained using Genetic Algorithms (GAs). The use of GAs helps automating the use of HMMs, by liberating users from the need of statistical knowledge, assumed by software that trains HMMs from data. The number of stat...

Consider a stationary precise hidden Markov model (HMM) with n hidden states Xk, taking values xk in a set {1, . . . ,m} and n observations Ok, taking values ok. Both the marginal model pX1(x1), the emission models pOk|Xk(ok|xk) and the transition models pXk|Xk−1(xk|xk−1) are unknown. We can then use the Baum–Welch algorithm [see, e.g., 4] to get a maximum-likelihood estimate of these models. T...

In this paper we describe an unsupervised learning algorithm for automatically training a rule-based part of speech tagger without using a manually tagged corpus. We compare this algorithm to the Baum-Welch algorithm, used for unsupervised training of stochastic taggers. Next, we show a method for combining unsupervised and supervised rule-based training algorithms to create a highly accurate t...