• Publications
  • Influence
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
TLDR
This paper explains the probabilistic and geometric meanings of correntropy, and shines light on its connections with M-estimation, ITL, and kernel methods, hoping the insights gained here will be helpful in other research contexts. Expand
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Information Theoretic Learning - Renyi's Entropy and Kernel Perspectives
  • J. Príncipe
  • Computer Science, Mathematics
  • Information Theoretic Learning
  • 6 April 2010
TLDR
This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms for improved performance without using full-blown Bayesian approaches that require a much larger computational cost. Expand
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Kernel Adaptive Filtering: A Comprehensive Introduction
TLDR
Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Expand
  • 442
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Support vector machines for SAR automatic target recognition
TLDR
Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. Expand
  • 371
  • 54
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Information Theoretic Learning
TLDR
Learning systems depend on three interrelated components: topologies, cost/performance functions, and learning algorithms. Expand
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Generalized correlation function: definition, properties, and application to blind equalization
TLDR
In this paper, a new generalized correlation measure is developed that includes the information of both the distribution and that of the time structure of a stochastic process. Expand
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The Kernel Least-Mean-Square Algorithm
TLDR
The combination of the famed kernel trick and the least-mean-square algorithm provides an interesting sample-by-sample update for an adaptive filter in reproducing kernel Hilbert spaces (RKHS), which is named in this paper the KLMS. Expand
  • 310
  • 29
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Neural and adaptive systems : fundamentals through simulations
Data Fitting with Linear Models. Pattern Recognition. Multilayer Perceptrons. Designing and Training MLPs. Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines.Expand
  • 469
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Quantized Kernel Least Mean Square Algorithm
TLDR
We propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering, which is based on a simple online vector quantization method. Expand
  • 314
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An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems
The paper investigates error-entropy-minimization in adaptive systems training. We prove the equivalence between minimization of error's Renyi (1970) entropy of order /spl alpha/ and minimization ofExpand
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