Results 1 - 10 of 8,043

Iterative decoding of binary block and convolutional codes

by Joachim Hagenauer, Elke Offer, Lutz Papke - IEEE TRANS. INFORM. THEORY , 1996
"... Iterative decoding of two-dimensional systematic convolutional codes has been termed “turbo” (de)coding. Using log-likelihood algebra, we show that any decoder can he used which accepts soft inputs-including a priori values-and delivers soft outputs that can he split into three terms: the soft chann ..."
Abstract - Cited by 610 (43 self) - Add to MetaCart
stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes

Fast and robust fixed-point algorithms for independent component analysis

by Aapo Hyvärinen - IEEE TRANS. NEURAL NETW , 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
Abstract - Cited by 884 (34 self) - Add to MetaCart
information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information

An Empirical Study of Smoothing Techniques for Language Modeling

by Stanley F. Chen , 1998
"... We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e.g., Br ..."
Abstract - Cited by 1224 (21 self) - Add to MetaCart
.g., Brown versus Wall Street Journal), and n-gram order (bigram versus trigram) affect the relative performance of these methods, which we measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very

K.B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classication Learning. In:

by Keki B Irani , Usama M Fayyad - IJCAI. , 1993
"... Abstract Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued a ..."
Abstract - Cited by 832 (7 self) - Add to MetaCart
Abstract Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous

Stacked generalization

by David H. Wolpert - NEURAL NETWORKS , 1992
"... This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second sp ..."
Abstract - Cited by 731 (9 self) - Add to MetaCart
This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second

The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1999

by Amos Bairoch, Rolf Apweiler - Nucleic Acids Res , 1999
"... SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domain structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other ..."
Abstract - Cited by 624 (5 self) - Add to MetaCart
SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domain structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration

The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000

by Amos Bairoch, Rolf Apweiler - Nucleic Acids Res , 2000
"... SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with othe ..."
Abstract - Cited by 773 (21 self) - Add to MetaCart
SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration

On active contour models and balloons

by D. Cohen - CVGIP: Image
"... The use.of energy-minimizing curves, known as “snakes, ” to extract features of interest in images has been introduced by Kass, Witkhr & Terzopoulos (Znt. J. Comput. Vision 1, 1987,321-331). We present a model of deformation which solves some of the problems encountered with the original method. ..."
Abstract - Cited by 588 (43 self) - Add to MetaCart
The use.of energy-minimizing curves, known as “snakes, ” to extract features of interest in images has been introduced by Kass, Witkhr & Terzopoulos (Znt. J. Comput. Vision 1, 1987,321-331). We present a model of deformation which solves some of the problems encountered with the original method

Clustering with Bregman Divergences

by Arindam Banerjee, Srujana Merugu, Inderjit Dhillon, Joydeep Ghosh - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... A wide variety of distortion functions are used for clustering, e.g., squared Euclidean distance, Mahalanobis distance and relative entropy. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergence ..."
Abstract - Cited by 443 (57 self) - Add to MetaCart
A wide variety of distortion functions are used for clustering, e.g., squared Euclidean distance, Mahalanobis distance and relative entropy. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman

Efficient Implementation of Weighted ENO Schemes

by Guang-shan Jiang, Chi-wang Shu , 1995
"... In this paper, we further analyze, test, modify and improve the high order WENO (weighted essentially non-oscillatory) finite difference schemes of Liu, Osher and Chan [9]. It was shown by Liu et al. that WENO schemes constructed from the r th order (in L¹ norm) ENO schemes are (r +1) th order accur ..."
Abstract - Cited by 412 (38 self) - Add to MetaCart
accurate. We propose a new way of measuring the smoothness of a numerical solution, emulating the idea of minimizing the total variation of the approximation, which results in a 5th order WENO scheme for the case r = 3, instead of the 4th order with the original smoothness measurement by Liu et al. This 5
Results 1 - 10 of 8,043