This paper presents the capacity of a modulo-sum simple relay network. In previous work related to this paper, capacity was characterized for the case where the noise was transmitted to the relay. And the closed-form capacity was derived only for the noise with a Bernoulli-( distribution. However, in this paper, the source is transmitted to the relay, and a more general case of noise with an ar...

This thesis presents new approaches in off-line Arabic Handwriting Recognition based on conventional Bernoulli Hidden Markov models. Until now, the off-line handwriting recognition, in particular, the Arabic handwriting recognition is still far away form being perfect. Hidden Markov Models (HMMs) are now widely used for off-line handwriting recognition in many languages and, in particular, in A...

Hidden Markov Models (HMMs) are now widely used for off-line handwriting recognition in many languages. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, where state-conditional probability density functions in each HMM are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kind of features should be used an...

We investigate a population of binary mistake sequences that result from learning with parametric models of different order. We obtain estimates of their error, algorithmic complexity and divergence from a purely random Bernoulli sequence. We study the relationship of these variables to the learner’s information density parameter which is defined as the ratio between the lengths of the compress...

A memory control for T-S fuzzy discrete-time systems with sto- chastic input delay is proposed in this paper. Dierent from the common assumptions on the time delay in the existing literatures, it is assumed in this paper that the delays vary randomly and satisfy some probabilistic dis- tribution. A new state space model of the discrete-time T-S fuzzy system is derived by introducing some stocha...

Generalized multilinear model for dimensionality reduction of binary tensors (GMM-DR-BT) is a technique for computing low-rank approximations of multi-dimensional data objects, tensors. The model exposes a latent structure that represents dominant trends in the binary tensorial data while retaining as much information as possible. Recently, there exist several models for computing the low-rank ...

We study the capacity of a binary multiple input multiple output (MIMO) channel with interference. Interference is modelled as a non-linear relation between the input and the output words of the system. Namely, we assume that the fading coefficients in the channel matrix are binary and drawn with i.i.d. Bernoulli probabilities, and any element of the output word is given by the minimum value (t...

Multi-label classification is an important machine learning task wherein one assigns a subset of candidate labels to an object. In this paper, we propose a new multi-label classification method based on Conditional Bernoulli Mixtures. Our proposed method has several attractive properties: it captures label dependencies; it reduces the multi-label problem to several standard binary and multi-cla...