Theoretical Advances In Neural Computation And Learning
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Theoretical Advances In Neural Computation And Learning
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Author : Vwani Roychowdhury
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Theoretical Advances In Neural Computation And Learning written by Vwani Roychowdhury and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.
Algorithmic Learning Theory
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Author : Hiroki Arimura
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-11-15
Algorithmic Learning Theory written by Hiroki Arimura and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-11-15 with Computers categories.
This book constitutes the refereed proceedings of the 11th International Conference on Algorithmic Learning Theory, ALT 2000, held in Sydney, Australia in December 2000. The 22 revised full papers presented together with three invited papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.
Algorithmic Learning Theory
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Author : Naoki Abe
language : en
Publisher: Springer
Release Date : 2003-06-30
Algorithmic Learning Theory written by Naoki Abe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-30 with Computers categories.
This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25–28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2226).
Algorithmic Learning Theory
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Author : Michael M. Richter
language : en
Publisher: Springer
Release Date : 2003-06-29
Algorithmic Learning Theory written by Michael M. Richter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ ̈aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses.
Current Trends In Theoretical Computer Science
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Author : Gheorghe P?un
language : en
Publisher: World Scientific
Release Date : 2001
Current Trends In Theoretical Computer Science written by Gheorghe P?un and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.
The scientific developments at the end of the past millennium were dominated by the huge increase and diversity of disciplines with the common label "computer science". The theoretical foundations of such disciplines have become known as theoretical computer science. This book highlights some key issues of theoretical computer science as they seem to us now, at the beginning of the new millennium. The text is based on columns and tutorials published in the Bulletin of the European Association for Theoretical Computer Science in the period 1995 -- 2000. The columnists themselves selected the material they wanted for the book, and the editors had a chance to update their work. Indeed, much of the material presented here appears in a form quite different from the original. Since the presentation of most of the articles is reader-friendly and does not presuppose much knowledge of the area, the book constitutes suitable supplementary reading material for various courses in computer science.
Computational Learning Theory
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Author : Jyrki Kivinen
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-06-26
Computational Learning Theory written by Jyrki Kivinen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-06-26 with Computers categories.
This book is tailored for students and professionals as well as novices from other fields to mass spectrometry. It will guide them from the basics to the successful application of mass spectrometry in their daily research. Starting from the very principles of gas-phase ion chemistry and isotopic properties, it leads through the design of mass analyzers and ionization methods in use to mass spectral interpretation and coupling techniques. Step by step the readers will learn how mass spectrometry works and what it can do as a powerful tool in their hands. The book comprises a balanced mixture of practice-oriented information and theoretical background. The clear layout, a wealth of high-quality figures and a database of exercises and solutions, accessible via the publisher's web site, support teaching and learning.
Theoretical Computer Science Proceedings Of The Fifth Italian Conference
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Author : Alfredo De Santis
language : en
Publisher: World Scientific
Release Date : 1996-04-13
Theoretical Computer Science Proceedings Of The Fifth Italian Conference written by Alfredo De Santis and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-04-13 with Computers categories.
The Fifth Italian Conference on Theoretical Computer Science covers all aspects of Theoretical Computer Science. Among the topics addressed in the volume are Algorithms, Concurrency, Automata, Formal Languages, Computational Complexity, Temporal and Model Logic, Logic Programming, and λ-Calculus.The proceedings include 33 selected papers and three distinguished invited lectures by Michael Luby, Ugo Montanari and Alberto Bertoni.
Latin 2000 Theoretical Informatics
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Author : Gaston H. Gonnet
language : en
Publisher: Springer
Release Date : 2007-04-11
Latin 2000 Theoretical Informatics written by Gaston H. Gonnet and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-11 with Computers categories.
This book constitutes the refereed proceedings of the 4th International Conference, Latin American Theoretical Informatics, LATIN 2000, held in Punta del Est, Uruguay, in April 2000. The 42 revised papers presented were carefully reviewed and selected from a total of 87 submissions from 26 countries. Also included are abstracts or full papers of several invited talks. The papers are organized in topical sections on random structures and algorithms, complexity, computational number theory and cryptography, algebraic algorithms, computability, automata and formal languages, and logic and programming theory.
Learning And Generalisation
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Author : Mathukumalli Vidyasagar
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Learning And Generalisation written by Mathukumalli Vidyasagar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Technology & Engineering categories.
Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: • How does a machine learn a concept on the basis of examples? • How can a neural network, after training, correctly predict the outcome of a previously unseen input? • How much training is required to achieve a given level of accuracy in the prediction? • How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time? The second edition covers new areas including: • support vector machines; • fat-shattering dimensions and applications to neural network learning; • learning with dependent samples generated by a beta-mixing process; • connections between system identification and learning theory; • probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms. It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.
Handbook Of Approximation Algorithms And Metaheuristics
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Author : Teofilo F. Gonzalez
language : en
Publisher: CRC Press
Release Date : 2018-05-15
Handbook Of Approximation Algorithms And Metaheuristics written by Teofilo F. Gonzalez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-15 with Computers categories.
Handbook of Approximation Algorithms and Metaheuristics, Second Edition reflects the tremendous growth in the field, over the past two decades. Through contributions from leading experts, this handbook provides a comprehensive introduction to the underlying theory and methodologies, as well as the various applications of approximation algorithms and metaheuristics. Volume 1 of this two-volume set deals primarily with methodologies and traditional applications. It includes restriction, relaxation, local ratio, approximation schemes, randomization, tabu search, evolutionary computation, local search, neural networks, and other metaheuristics. It also explores multi-objective optimization, reoptimization, sensitivity analysis, and stability. Traditional applications covered include: bin packing, multi-dimensional packing, Steiner trees, traveling salesperson, scheduling, and related problems. Volume 2 focuses on the contemporary and emerging applications of methodologies to problems in combinatorial optimization, computational geometry and graphs problems, as well as in large-scale and emerging application areas. It includes approximation algorithms and heuristics for clustering, networks (sensor and wireless), communication, bioinformatics search, streams, virtual communities, and more. About the Editor Teofilo F. Gonzalez is a professor emeritus of computer science at the University of California, Santa Barbara. He completed his Ph.D. in 1975 from the University of Minnesota. He taught at the University of Oklahoma, the Pennsylvania State University, and the University of Texas at Dallas, before joining the UCSB computer science faculty in 1984. He spent sabbatical leaves at the Monterrey Institute of Technology and Higher Education and Utrecht University. He is known for his highly cited pioneering research in the hardness of approximation; for his sublinear and best possible approximation algorithm for k-tMM clustering; for introducing the open-shop scheduling problem as well as algorithms for its solution that have found applications in numerous research areas; as well as for his research on problems in the areas of job scheduling, graph algorithms, computational geometry, message communication, wire routing, etc.