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...

In this paper, we derive an algorithm similar to the well-known Baum–Welch algorithm for estimating the parameters of a hidden Markov model (HMM). The new algorithm allows the observation PDF of each state to be defined and estimated using a different feature set. We show that estimating parameters in this manner is equivalent to maximizing the likelihood function for the standard parameterizat...

In this paper several results concerning static hand gesture recognition using an algorithm based on left-right Hidden Markov Models (HMM) are presented. The features used as observables in the training as well as in the recognition phases are based either on the 2D Discrete Cosine Transform (DCT) or on the Principal Component Analysis (PCA). The left-right topology of the HMM together with the...

A and an analysis are given for an experimental performance evaluation of WASSP, an automated wavelet-based spectral method for constructing an approximate confidence interval on the steady-state mean of a simulation output process such that the delivered confidence interval satisfies user-specified requirements on absolute or relative precision as well as coverage probability. The experimentat...

Hidden Markov Models are commonly trained using algorithms derived from gradient-based methods such as the Baum-Welch procedure. We describe a new representation of discrete observation HMMs that permits them to be trained using Population-Based Incremental Learning (PBIL), a variant of genetic learning that combines evolutionary optimization and hill-climbing Baluja and Caruana, 1995]. In this...

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 presents new application of Particle Swarm Optimization (PSO) algorithm for training Hidden Markov Models (HMMs). The problem of finding an optimal set of model parameters is numerical optimization problem constrained by stochastic character of HMM parameters. Constraint handling is carried out using three different ways and the results are compared to Baum-Welch algorithm (BW), comm...

This paper presents an algorithm for learning the underlying models which generate streams of observations, found in video data, which encode activities performed by a person who appears in the video. With these learned models, we then aim to carry out recognition in new video streams which display the same activities as the ones that were learned. Our algorithm represents the underlying models...

We study an equivariant co-assembly map that is dual to the usual Baum–Connes assembly map and closely related to coarse geometry, equivariant Kasparov theory, and the existence of dual Dirac morphisms. As applications, we prove the existence of dual Dirac morphisms for groups with suitable compactifications, that is, satisfying the Carlsson–Pedersen condition, and we study a K–theoretic counte...