Hidden Markov modeling (HMM) techniques have been applied in the past few years to characterize single ion channel current events at low signal-to-noise ratios (SNR’s). In this paper, an adaptation of the forward-backward procedure and Baum–Welch algorithm is presented to model ion channel kinetics under conditions of correlated and state-dependent excess noise like that observed in patch-clamp...

Hidden Markov models (HMM’s) are popular in many applications, such as automatic speech recognition, control theory, biology, communication theory over channels with bursts of errors, queueing theory, and many others. Therefore, it is important to have robust and fast methods for fitting HMM’s to experimental data (training). Standard statistical methods of maximum likelihood parameter estimati...

Estimation of distribution algorithm (EDA) can solve more complicated problems than its predecessor (Genetic Algorithm). EDA uses various methods to probabilistically model a group of highly fit individuals. Calculating the model in sophisticated EDA is very time consuming. To reduce the model building time, the authors propose compressed chromosome encoding. A chromosome is encoded using a for...

The extended Baum-Welch (EBW) is the most popular algorithm for discriminative training of speech recognition acoustic models. The EBW algorithm is usually controlled with heuristic rules, which are used to determine the smoothing parameters of the algorithm. In this paper we propose a control method for EBW which is based on the optimization of an error measure over a small control set. The la...

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....

– In 1983, Welch, L.R. and Berlekamp, E.R. introduced a new key equation based on rational interpolation for algebraically decoding Reed-Solomon (RS) codes over finite fields. A key feature of their so-called remainder decoding approach is that the computation of the syndrome vector corresponding to a given received word is not needed. For long codes, this implies significant computational savi...

The predominant learning strategy for H(S)MMs is local search heuristics, of which the Baum-Welch/ expectation maximization (EM) algorithm is mostly used. It is an iterative learning procedure starting with a predefined topology and randomly-chosen initial parameters. However, state-of-the-art approaches based on arbitrarily defined state numbers and parameters can cause the risk of falling int...

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Fi...

There is an increasing demand for systems which handle higher density, additional loads as seen in storage workload modelling, where workloads can be characterized on-line. This paper aims to find a workload model which processes incoming data and then updates its parameters "on-the-fly." Essentially, this will be an incremental hidden Markov model (IncHMM) with an improved Baum-Welch algorithm...