In this paper, a simplified 2-D second-order Hidden Markov Model (HMM) with tied state mixtures is applied to the face recognition problem. The mixture of the model states is fully-tied across all models for lower complexity. Tying HMM parameters is a well-known solution for the problem of insufficient training data leading to nonrobust estimation. We show that parameter tying in HMM also enhan...

A theory of the random magnetic mixture of some kinds of Ising spins is formulated in terms of distribution functions which are introduced for the thermal averages of each kind of spins. The distribution functions are determined from a simultaneous non-linE)ar integral equation of order n, where n is the number of kinds of spins. By the use of this theory, the magnetic properties of a honeycomb...

Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distributionof the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can als...

Clustering p-dimensional data by fitting a mixture of K normals has enjoyed renewed interest (for example, see Splus function “mclust”). However, the number of parameters for the model grows rapidly with dimension p. For example, even if all the covariance matrices are assumed to be equal, the number of parameters is (K − 1)+K ∗ p+ p(p+1)/2 for the weights, means and covariance matrix. At ACAS ...

Gaussian Mixture Models (GMMs) has been proposed for off-line signature verification. The individual Gaussian components are shown to represent some global features such as skewness, kurtosis, etc. that characterize various aspects of a signature, and are effective for modeling its specificity. The learning phase involves the use of Gaussian Mixture Model (GMM) technique to build a reference mo...

The following describes recent work on the Lincoln CSR system. Some new variations in semiphone modeling have been tested. A very simple improved duration model has reduced the error rate by about 10~ in both triphone and semiphone systems. A new training strategy has been tested which, by itself, did not provide useful improvements but suggests that improvements can be obtained by a related ra...

In this paper we address the problem of topic classification of speech data. Our concern in this paper is the situation in which there is no speech or phoneme recognizer available for the domain of the speech data. In this situation the only inputs for training the system are audio speech files labeled according to the topics of interest. The process that we follow in developing the topic class...

RubyStar is a dialog system designed to create “human-like” conversation by combining different response generation strategies. RubyStar conducts a nontask-oriented conversation on general topics by using an ensemble of rule-based, retrieval-based and generative methods. Topic detection, engagement monitoring, and context tracking are used for managing interaction. Predictable elements of conve...

In this paper we proposed Hierarchical Gaussian Mixture Model (HGMM) a novel type of Gaussian Mixture which detects network based attacks as anomalies using statistical preprocessing classification. This method learns patterns of normal and intrusive activities to classify that use a set of Gaussian probability distribution functions. The use of Maximum likelihood in detection phase has used th...