The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM...

We present new algorithms for parameter estimation of HMMs. By adapting a framework used for supervised learning, we construct iterative algorithms that maximize the likelihood of the observations while also attempting to stay “close” to the current estimated parameters. We use a bound on the relative entropy between the two HMMs as a distance measure between them. The result is new iterative t...

We address the problem of learning discrete hidden Markov models from very long sequences of observations. Incremental versions of the Baum-Welch algorithm that approximate the β-values used in the backward procedure are commonly used for this problem, since their memory complexity is independent of the sequence length. We introduce an improved incremental Baum-Welch algorithm with a new backwa...

Training probabilistic finite automata with the EM/Baum-Welch algorithm is computationally very intensive, especially if random ergodic automata are used initially, and additional strategies such as deterministic annealing are used. In this paper we present some optimization and parallelization strategies to the Baum-Welch algorithm that often allow for training of much larger automata with a l...

the air transport industry is seeking to manage risks in air travels. its main objective is to detect abnormal behaviors in various flight conditions. the current methods have some limitations and are based on studying the risks and measuring the effective parameters. these parameters do not remove the dependency of a flight process on the time and human decisions. in this paper, we used an hmm...

-This paper describes a technique for on-line signature verification using Hidden Markov Models (HMMs). Signatures are captured and digitized in real-time using a graphic tablet. For each signature a HMM is constructed using a set of sample signatures described by the normalized directional angle function of the distance along the signature trajectory. The Baum-Welch algorithm is used for both ...

In this paper, we employ the concept of HMM-Sufficient Statistics (HMM-Suff Stat) and N-best speakers selection to realize a rapid implementation of Baum-Welch and MLLR. Only a single arbitrary utterance is required which is used to select the N-best speakers HMM-Suff Stat from the training database as adaptation data. Since HMM-Suff Stat are pre-computed offline, computation load is minimized....