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16
Informationtheoretic asymptotics of Bayes methods
 IEEE Transactions on Information Theory
, 1990
"... AbstractIn the absence of knowledge of the true density function, Bayesian models take the joint density function for a sequence of n random variables to be an average of densities with respect to a prior. We examine the relative entropy distance D,, between the true density and the Bayesian densit ..."
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Cited by 107 (10 self)
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AbstractIn the absence of knowledge of the true density function, Bayesian models take the joint density function for a sequence of n random variables to be an average of densities with respect to a prior. We examine the relative entropy distance D,, between the true density and the Bayesian density and show that the asymptotic distance is (d/2Xlogn)+ c, where d is the dimension of the parameter vector. Therefore, the relative entropy rate D,,/n converges to zero at rate (logn)/n. The constant c, which we explicitly identify, depends only on the prior density function and the Fisher information matrix evaluated at the true parameter value. Consequences are given for density estimation, universal data compression, composite hypothesis testing, and stockmarket portfolio selection. 1.
Prediction of human mRNA donor and acceptor sites from the DNA sequence
 J. Mol. Biol
, 1991
"... Artificial neural networks have been applied to the prediction of splice site location in human premRNA. A joint prediction scheme where prediction of transition regions between introns and exons regulates a cutoff level for splice site assignment was able to predict splice site locations with con ..."
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Cited by 103 (8 self)
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Artificial neural networks have been applied to the prediction of splice site location in human premRNA. A joint prediction scheme where prediction of transition regions between introns and exons regulates a cutoff level for splice site assignment was able to predict splice site locations with confidence levels far better than previously reported in the literature. The problem of predicting donor and acceptor sites in human genes is hampered by the presence of numerous amounts of false positives  in the paper the distribution of these false splice sites is examined and linked to a possible scenario for the splicing mechanism in vivo. When the presented method detects 95% of the true donor and acceptor sites it makes less than 0.1% false donor site assignments and less than 0.4% false acceptor site assignments. For the large data set used in this study this means that on the average there are one and a half false donor sites per true donor site and six false acceptor ...
Chou K: Prediction of protein secondary structure content. Protein Eng
, 1999
"... The GOR program for predicting protein secondary structure is extended to include triple correlation. A score system for a residue pair to be at certain conformation state is derived from the conditional weight matrix describing amino acid frequencies at each position of a window flanking the pair u ..."
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Cited by 7 (0 self)
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The GOR program for predicting protein secondary structure is extended to include triple correlation. A score system for a residue pair to be at certain conformation state is derived from the conditional weight matrix describing amino acid frequencies at each position of a window flanking the pair under the condition for the pair to be at the fixed state. A program using this score system to predict protein secondary structure is established. After training the model with a learning set created from PDB SELECT, the program is tested with two test sets. As a method using single sequence for predicting secondary structures, the approach achieves a high accuracy near 70%. PACS number(s): 87.10.+e,02.50.r 1
Algorithm for blind signal separation and recovery in static and dynamics environments
 Proc. IEEE Symposium on Circuits and Systems
, 1997
"... We propose update laws for the problem of blind separation in static and dynamic environments. The energy function is based on an approximation of the mutual information as a measure of independence. Both feedforward and feedback structures of the neural network are considered. A general framework t ..."
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Cited by 6 (1 self)
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We propose update laws for the problem of blind separation in static and dynamic environments. The energy function is based on an approximation of the mutual information as a measure of independence. Both feedforward and feedback structures of the neural network are considered. A general framework to develop the update law for the dynamic model is proposed. Computer simulations to support the analytical work are provided. 1.
The Robustness of ContentBased Search in Hierarchical Peer to Peer Networks
 In Proceedings of the 13 th International Conference on Information and Knowledge Management (CIKM'04). RevConnect
, 2004
"... Hierarchical peer to peer networks with multiple directory services are an important architecture for largescale file sharing due to their effectiveness and efficiency. Recent research argues that they are also an effective method of providing largescale contentbased federated search of textbase ..."
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Cited by 6 (1 self)
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Hierarchical peer to peer networks with multiple directory services are an important architecture for largescale file sharing due to their effectiveness and efficiency. Recent research argues that they are also an effective method of providing largescale contentbased federated search of textbased digital libraries. In both cases the directory services are critical resources that are subject to attack or failure, but the latter architecture may be particularly vulnerable because content is less likely to be replicated throughout the network.
Spatially Adaptive Estimation via Fitted Local Likelihood Techniques
"... Abstract—This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modeling of observations and estimated signals. The approach is based on the assumption of a local homogeneity of the signal: for every point there exists a neighborhood ..."
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Cited by 2 (0 self)
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Abstract—This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modeling of observations and estimated signals. The approach is based on the assumption of a local homogeneity of the signal: for every point there exists a neighborhood in which the signal can be well approximated by a constant. The fitted local likelihood statistics are used for selection of an adaptive size and shape of this neighborhood. The algorithm is developed for a quite general class of observations subject to the exponential distribution. The estimated signal can be uni and multivariable. We demonstrate a good performance of the new algorithm for image denoising and compare the new method versus the intersection of confidence interval (ICI) technique that also exploits a selection of an adaptive neighborhood for estimation. Index Terms—Adaptive nonGaussian image denoising, adaptive nonparametric regression, anisotropic imaging, fitted local likelihood (FLL), nonGaussian denoising, Poissonian denoising, varying threshold parameters. I.
VOT 74017 PROTEIN SECONDARY STRUCTURE PREDICTION FROM AMINO ACID SEQUENCE USING ARTIFICIAL INTELLIGENCE TECHNIQUE
, 2007
"... Large genome sequencing projects generate huge number of protein sequences in their primary structures that is difficult for conventional biological techniques to determine their corresponding 3D structures and then their functions. Protein secondary structure prediction is a prerequisite step in de ..."
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Large genome sequencing projects generate huge number of protein sequences in their primary structures that is difficult for conventional biological techniques to determine their corresponding 3D structures and then their functions. Protein secondary structure prediction is a prerequisite step in determining the 3D structure of a protein. In this research a method for prediction of protein secondary structure has been proposed and implemented together with other known accurate methods in this domain. The method has been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. A benchmark data set is exploited in training and testing the methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GORV information theory and the power of the neural network to classify a novel protein sequence in one of its three secondary structures classes. NNGORVI is developed and implemented to predict proteins secondary structure using the biological information conserved in neighboring residues and related
LETTER Communicated by Jose C. Principe Error Entropy in Classification Problems: A Univariate Data Analysis
"... Entropybased cost functions are enjoying a growing attractiveness in unsupervised and supervised classification tasks. Better performances in terms both of error rate and speed of convergence have been reported. In this letter, we study the principle of error entropy minimization (EEM) from a theor ..."
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Entropybased cost functions are enjoying a growing attractiveness in unsupervised and supervised classification tasks. Better performances in terms both of error rate and speed of convergence have been reported. In this letter, we study the principle of error entropy minimization (EEM) from a theoretical point of view. We use Shannon’s entropy and study univariate data splitting in twoclass problems. In this setting, the error variable is a discrete random variable, leading to a not too complicated mathematical analysis of the error entropy. We start by showing that for uniformly distributed data, there is equivalence between the EEM split and the optimal classifier. In a more general setting, we prove the necessary conditions for this equivalence and show the existence of class configurations where the optimal classifier corresponds to maximum error entropy. The presented theoretical results provide practical guidelines that are illustrated with a set of experiments with both real and simulated data sets, where the effectiveness of EEM is compared with the usual mean square error minimization.
Applications of density matrices in a trapped Bose gas
, 2006
"... An overview of the BoseEinstein condensation of correlated atoms in a trap is presented by examining the effect of interparticle correlations to one and twobody properties of the above systems at zero temperature in the framework of the lowest order cluster expansion. Analytical expressions for t ..."
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An overview of the BoseEinstein condensation of correlated atoms in a trap is presented by examining the effect of interparticle correlations to one and twobody properties of the above systems at zero temperature in the framework of the lowest order cluster expansion. Analytical expressions for the one and twobody properties of the Bose gas are derived using Jastrowtype correlation function. In addition numerical calculations of the natural orbitals and natural occupation numbers are also carried out. Special effort is devoted for the calculation of various quantum information properties including Shannon entropy, Onicescu informational energy, KullbackLeibler relative entropy and the recently proposed JensenShannon divergence entropy. The above quantities are calculated for the trapped Bose gases by comparing the correlated and uncorrelated cases as a function of the strength of the shortrange correlations. The GrossPiatevskii equation is solved giving the density distributions in position and momentum space, which are employed to calculate quantum information properties of the Bose gas.