We prove the Courtade-Kumar conjecture, for several classes of n-dimensional Boolean functions, for all n ≥ 2 and for all values of the error probability of the binary symmetric channel, 0 ≤ p ≤ 1 2 . This conjecture states that the mutual information between any Boolean function of an n-dimensional vector of independent and identically distributed inputs to a memoryless binary symmetric channe...

In present paper we study the use of the expectation maximization (EM) algorithm in classi cation. The EM-algorithm is used to calculate the probability of each vector belonging to each class. If we assign each vector to the class of maximal probability we get a classi cation minimizing a certain log-likelihood function. By analyzing these probabilities we get a clearer picture of how well data...

We prove the Courtade-Kumar conjecture, which states that the mutual information between any Boolean function of an n-dimensional vector of independent and identically distributed inputs to a memoryless binary symmetric channel and the corresponding vector of outputs is upper-bounded by 1 − H(p), where H(p) represents the binary entropy function. That is, let X = [X1 . . . Xn] be a vector of in...

In this paper, a new LSP speech parameter compression scheme is proposed which uses conditional probability information through classification. For efficient compression of speech LSP parameter vectors it is essential that higher order correlations are exploited. The use of conditional probability information has been hindered by high complexity of the information. For example, a LSP vector has...

The field reject ratio, the fraction of defective devices that pass the acceptance test, is a measure of the quality of the tested product. Although the assessment of quality is important, a n accurate measurement of the field reject ratio of tested VLSI chips is often not feasible. We show that the known methods of field reject ratio prediction a re not accurate since they fail to realisticall...

This paper presents the new algorithm of the recursive least-squares (RLS) Wiener fixed-point smoother and filter based on the randomly delayed observed values by one sampling time in linear discretetime wide-sense stationary stochastic systems. The observed value ) (k y consists of the observed value ) 1 (  k y with the probability ) (k p and of ) (k y with the probability ). ( 1 k p  It is ...

Since compared with the Support Vector Machine (SVM), the Relevance Vector Machine (RVM) not only has the advantage of avoiding the overlearn which is the characteristic of the SVM, but also greatly reduces the amount of computation of the kernel function and avoids the defects of the SVM that the scarcity is not strong, the large amount of calculation as well as the kernel function must satisf...

This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that are assumed to be mutually-exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ra...

In this correspondence, a method for voiced (V), unvoiced (UV), or silence (S) classification of speech segments, based on the maximum a posteriori probability criterion, is presented. The a poste-riori probabilities of the three classes are determined using a vector x = (fi,. .. ,ff) of measurements on the segment under consideration. It is assumed that the vector x has an L-dimensional Gaussi...