A low-complexity recursive procedure is presented for model selection and minimum mean squared error (MMSE) estimation in linear regression. Emphasis is given to the case of a sparse parameter vector and fewer observations than unknown parameters. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both a set of high posterior probability models and ...

Within the framework of speaker-adaptation, a technique based on tree structure and the maximum a posteriori criterion was proposed (SMAP). In SMAP, the parameters estimation, at each node in the tree is based on the assumption that the mismatch between the training and adaptation data is a Gaussian PDF which parameters are estimated by using the Maximum Likelihood criterion. To avoid poor tran...

An e2cient and 3exible parameter estimation scheme for grey-box models in the sense of discretely, partially observed Itô stochastic di6erential equations with measurement noise is presented along with a corresponding software implementation. The estimation scheme is based on the extended Kalman 9lter and features maximum likelihood as well as maximum a posteriori estimation on multiple indepen...

This paper presents a new approach to the estimation of 2-D motion vector fields from time-varying images. The approach is stochastic both in its formulation and in the solution method. The formulation involves the specification of a deterministic structural model along ivith stochastic observation and motion field models. Two motion models are proposed: a globally smooth model based on vector ...

MOTIVATION Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic c...

One problem with speech recognition is a low performance in noisy environments because it is easily influenced by aerial in air. Although the sound quality of body-conducted speech (BCS) and regular speech are different, with BCS recognition, it is possible to recognize an utterance in noisy environments with a rating of 98 dB sound pressure level (SPL) in our previous study. In this study, we ...

Many machine learning tasks can be formulated in terms of predicting structured outputs. In frameworks such as the structured support vector machine (SVM-Struct) and the structured perceptron, discriminative functions are learned by iteratively applying efficient maximum a posteriori (MAP) decoding. However, maximum likelihood estimation (MLE) of probabilistic models over these same structured ...

In this paper, we propose an unsupervised color image segmentation scheme using homotopy continuation method and Compound Markov Random Field (CMRF) model. The proposed scheme is recursive in nature where model parameter estimation and the image label estimation are alternated. Ohta (I1, I2, I3) model is used as the color model for image segmentation and we propose a compound MRF model taking c...

The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Text mining is the process of extracting interesting information and knowledge from unstructured text. One key difficulty with text classification learning algorithms is that they require many hand-labele...

Technologies such as solid-phase epitaxial regrowth and millisecond annealing techniques have led to a wide range of maximum temperatures and heating rates for activating dopants and eliminating ion implantation damage for transistor junction formation. Developing suitable annealing strategies depends on mathematical models that incorporate accurate defect physics. The present work describes a ...