WorldCat Identities

Príncipe, J. C. (José C.)

Overview
Works: 46 works in 162 publications in 1 language and 3,211 library holdings
Genres: Conference papers and proceedings 
Roles: Author, Editor, Creator, Other
Publication Timeline
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Most widely held works by J. C Príncipe
Neural and adaptive systems : fundamentals through simulations by J. C Príncipe( Book )

18 editions published between 1999 and 2000 in English and held by 294 WorldCat member libraries worldwide

"This interactive book and CD-ROM will help you develop a better understanding of the behaviour of adaptive systems. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction." "Key Features of the Text: the text and CD combine to become an interactive learning tool; emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations; each key concept is followed by an interactive example; over 200 fully functional simulations of adaptive systems are included; the text and CD offer a unified view of neural networks, adaptive filters, pattern recognition and support vector machines; and hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts"--Jacket
Kernel adaptive filtering : a comprehensive introduction by J. C Príncipe( Book )

18 editions published between 2010 and 2014 in English and held by 135 WorldCat member libraries worldwide

Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering> is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. - Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm - Presents a powerful model-selection method called maximum marginal likelihood - Addresses the principal bottleneck of kernel adaptive filters'their growing structure - Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site - Concludes each chapter with a summary of the state of the art and potential future directions for original research> Kernel Adaptive Filtering> is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems
Information theoretic learning : Renyi's entropy and kernel perspectives by J. C Príncipe( Book )

14 editions published between 2010 and 2012 in English and held by 89 WorldCat member libraries worldwide

"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. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by scalars and functions with information theoretic underpinnings, respectively entropy, mutual information and correntropy. ITL quantifies the stochastic structure of the data beyond second order statistics for improved performance without using full-blown Bayesian approaches that require a much larger computational cost. This is possible because of a non-parametric estimator of Renyi's quadratic entropy that is only a function of pairwise differences between samples. The book compares the performance of ITL algorithms with the second order counterparts in many engineering and machine learning applications. Students, practitioners and researchers interested in statistical signal processing, computational intelligence, and machine learning will find in this book the theory to understand the basics, the algorithms to implement applications, and exciting but still unexplored leads that will provide fertile ground for future research. José C. Principe is Distinguished Professor of Electrical and Biomedical Engineering, and BellSouth Professor at the University of Florida, and the Founder and Director of the Computational NeuroEngineering Laboratory. He is an IEEE and AIMBE Fellow, Past President of the International Neural Network Society, Past Editor-in-Chief of the IEEE Trans. on Biomedical Engineering and the Founder Editor-in-Chief of the IEEE Reviews on Biomedical Engineering. He has written an interactive electronic book on Neural Networks, a book on Brain Machine Interface Engineering and more recently a book on Kernel Adaptive Filtering, and was awarded the 2011 IEEE Neural Network Pioneer Award."--Publisher's website
Brain-machine interface engineering by Justin Cort Sanchez( Book )

10 editions published in 2007 in English and Undetermined and held by 88 WorldCat member libraries worldwide

"Neural interfaces are one of the most exciting emerging technologies to impact bioengineering and neuroscience because they enable an alternate communication channel linking directly the nervous system with man-made devices. This book reveals the essential engineering principles and signal processing tools for deriving control commands from bioelectric signals in large ensembles of neurons. The topics featured include analysis techniques for determining neural representation, modeling in motor systems computing with neural spikes, and hardware implementation of neural interfaces. Beginning with an exploration of the historical developments that have led to the decoding of information from neural interfaces, this book compares the theory and performance of new neural engineering approaches for BMIs."--Jacket
Biocomputing( Book )

4 editions published in 2002 in English and held by 87 WorldCat member libraries worldwide

Advances in self-organizing maps : 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009 : proceedings by Pablo A Estévez( Book )

28 editions published between 2007 and 2013 in English and Undetermined and held by 83 WorldCat member libraries worldwide

"Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering and visualization purposes. Many SOM applications have been developed in engineering and science, and other fields. This book contains refereed papers presented at the 9th Workshop on Self-Organizing Maps (WSOM 2012) held at the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop brought together researchers and practitioners in the field of self-organizing systems. Among the book chapters there are excellent examples of the use of SOMs in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on SOMs as well as Learning Vector Quantization (LVQ) methods."--Back cover blurb
System parameter identification : information criteria and algorithms by Badong Chen( Book )

2 editions published in 2013 in English and held by 51 WorldCat member libraries worldwide

Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research provides a base for the book, but it incorporates the results from the latest international research publications. Named a 2013 Notable Computer Book for Information Systems by Computing Reviews One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developments Contains numerous illustrative examples to help the reader grasp basic methods
Spatiotemporal models in biological and artificial systems( Book )

6 editions published between 1996 and 1997 in English and held by 48 WorldCat member libraries worldwide

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : a new beginning for human health : 17-21 September, 2003, Cancún, Mexico by IEEE Engineering in Medicine and Biology Society( Book )

3 editions published in 2003 in English and held by 29 WorldCat member libraries worldwide

Independent component analysis and blind signal separation : 6th international conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006 : proceedings by Justinian P Rosca( Book )

9 editions published in 2006 in English and held by 5 WorldCat member libraries worldwide

This book constitutes the refereed proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2006, held in Charleston, SC, USA, in March 2006. The 120 revised papers presented were carefully reviewed and selected from 183 submissions. The papers are organized in topical sections on algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing
Brain-computer interfaces : an international assessment of research and development trends by Theodore W Berger( )

6 editions published in 2008 in English and Undetermined and held by 5 WorldCat member libraries worldwide

Brain-computer interface (BCI) research deals with establishing communication pathways between the brain and external devices where such pathways do not otherwise exist. Throughout the world, such research is surprisingly extensive and expanding. BCI research is rapidly approaching a level of first-generation medical practice for use by individuals whose neural pathways are damaged, and use of BCI technologies is accelerating rapidly in nonmedical arenas of commerce as well, particularly in the gaming, automotive, and robotics industries. The technologies used for BCI purposes are cutting-edge
Biocomputing( Book )

2 editions published in 2002 in English and held by 4 WorldCat member libraries worldwide

New directions in statistical signal processing : from systems to brain by Simon S Haykin( Book )

1 edition published in 2007 in English and held by 2 WorldCat member libraries worldwide

Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines. The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs)
Advances in self-organising maps( Book )

1 edition published in 2009 in English and held by 2 WorldCat member libraries worldwide

Automated detection and quantification of petit mal seizures in the electroencephalogram by J. C Príncipe( )

2 editions published in 1979 in English and held by 2 WorldCat member libraries worldwide

Acquisition and recognition of moving targets and enabling technologies : Volume 1 by Jian Li( Book )

2 editions published in 2001 in English and held by 1 WorldCat member library worldwide

State-of-the-art research on spectral estimation, feature extraction, and pattern recognition algorithms are presented for radar signal processing and automatic target recognition. Advanced space-time spectral estimation algorithms are presented for multiple moving target feature extraction as well as clutter and jamming suppression for airborne high range resolution (HRR) phased-array radar. A nonparametric adaptive filtering-based approach, referred to as the Gapped-data Amplitude and Phase Estimation (GAPES) algorithm, is proposed for the spectral analysis of gapped data sequences as well as synthetic aperture radar (SAR) imaging with angle diversity data fusion. A QUasi-parametric ALgorithm for target feature Extraction (QUALE) algorithm is also investigated for angle diversity data fusion. Support Vector Machines (SVMs) as compared with other advanced classifiers in the MSTAR Public Domain Release and HRR data are found to outperform neural networks and matched filters. A new concept to create negative examples from the known target class is presented and shown to tremendously improve the rejection of confusers. Finally, Information Theoretic Learning (ITL) is proposed as a new algorithm to demix HRR signatures of closely parked targets
Statistical pattern recognition for synthetic aperture radar (SAR)/automatic target recognition (ATR) : Volume 2 by Jian Li( Book )

2 editions published in 2001 in English and held by 1 WorldCat member library worldwide

State-of-the-art research on spectral estimation, feature extraction, and pattern recognition algorithms are presented for radar signal processing and automatic target recognition. Advanced space-time spectral estimation algorithms are presented for multiple moving target feature extraction as well as clutter and jamming suppression for airborne high range resolution (HRR) phased-array radar. A nonparametric adaptive filtering-based approach, referred to as the Gapped-data Amplitude and Phase EStimation (GAPES) algorithm, is proposed for the spectral analysis of gapped data sequences as well as synthetic aperture radar (SAR) imaging with angle diversity data fusion. A QUasi-parametric ALgorithm for target feature Extraction (QUALE) algorithm is also investigated for angle diversity data fusion. Support Vector Machines (SVMs) as compared with other advanced classifiers in the MSTAR Public Domain Release and HRR data are found to outperform neural networks and matched filters. A new concept to create negative examples from the known target class is presented and shown to tremendously improve the rejection of confusers. Finally, Information Theoretic Learning (ITL) is proposed as a new algorithm to demix HRR signatures of closely parked targets
Design and Implementation of Biologically Realistic Signal to Symbol Translators by J. C Príncipe( Book )

2 editions published in 2001 in English and held by 1 WorldCat member library worldwide

This paper reviews the problem of translating signals into symbols preserving maximally the information contained in the signal time structure. In this context we motivate the use of nonconvergent dynamics for the signal to symbol translator. We then describe a biologically realistic model of the olfactory system proposed by Walter Freemann that has locally stable dynamics but is globally chaotic. We present results of simulations and measurements obtained from a fabricated analog VLSI chip
Biocomputing by P. M Pardalos( )

2 editions published in 2002 in English and held by 0 WorldCat member libraries worldwide

In the quest to understand and model the healthy or sick human body, re­ searchers and medical doctors are utilizing more and more quantitative tools and techniques. This trend is pushing the envelope of a new field we call Biomedical Computing, as an exciting frontier among signal processing, pattern recognition, optimization, nonlinear dynamics, computer science and biology, chemistry and medicine. A conference on Biocomputing was held during February 25-27, 2001 at the University of Florida. The conference was sponsored by the Center for Applied Optimization, the Computational Neuroengineering Center, the Biomedical En­ gineering Program (through a Whitaker Foundation grant), the Brain Institute, the School of Engineering, and the University of Florida Research & Graduate Programs. The conference provided a forum for researchers to discuss and present new directions in Biocomputing. The well-attended three days event was highlighted by the presence of top researchers in the field who presented their work in Biocomputing. This volume contains a selective collection of ref­ ereed papers based on talks presented at this conference. You will find seminal contributions in genomics, global optimization, computational neuroscience, FMRI, brain dynamics, epileptic seizure prediction and cancer diagnostics. We would like to take the opportunity to thank the sponsors, the authors of the papers, the anonymous referees, and Kluwer Academic Publishers for making the conference successful and the publication of this volume possible. Panos M. Pardalos and Jose C
 
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Neural and adaptive systems : fundamentals through simulations
Alternative Names
Principe, J. C.

Príncipe, José

Príncipe, José C.

Príncipe, José C. 1950-

Príncipe, José Carlos 1950-

Languages
English (129)

Covers
Kernel adaptive filtering : a comprehensive introductionInformation theoretic learning : Renyi's entropy and kernel perspectivesBrain-machine interface engineeringBiocomputingAdvances in self-organizing maps : 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009 : proceedingsIndependent component analysis and blind signal separation : 6th international conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006 : proceedingsBrain-computer interfaces : an international assessment of research and development trendsBiocomputing