Abstract-The description of a novel type of rn-gram language model is given. The model offers, via a nonlinear recursive procedure, a com-putation and space efficient solution to the problem of estimating prob-abilities from sparse data. This solution compares favorably to other proposed methods

Yaroslavsky. His wide knowledge, patience and personal guidance throughout this work have been a great value for me. I feel very fortunate for having a supervisor like you. I wish to express my sincere gratitude to Barak Fishbain, for constructive and interesting discussions and for valuable advices and friendly help. i A common distortion in videos is image instability in the form of chaotic global and local displacements of image frames caused by camera instability, fluctuations in the refraction index of the light propagation media and similar factors. Such videos that very frequently present moving objects on a stable background contain tremendous redundancy that potentially can be used for image stabilization and perfecting provided reliable separation of stable background from true moving objects. Recently, it was proposed to use this redundancy for resolution enhancement of video through elastic registration, with sub-pixel accuracy, of segments of video frames which represent stable scenes.

and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process. Our minimum

Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification. We found that there always

and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our

An iterative method is given for solving Ax ~ffi b and minU Ax- b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable

A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas

Layout methods for dense and sparse data are often seen as two separate prob-lems with its own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures

This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance

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