Algorithmic Learning in a Random World: Vladimir Vovk, Alex Gammerman, Glenn Shafer

Algorithmic Learning in a Random World: Vladimir Vovk, Alex Gammerman, Glenn Shafer
Springer ISBN: 0387001522 2005-03-22 PDF (OCR) 324 pages 57.2 Mb

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

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Pattern Matching Algorithms

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Alberto Apostolico, Zvi Galil, «Pattern Matching Algorithms»
Oxford University Press | ISBN: 0195113675 | 1997 | PDF | 400 pages | 19.65 MB

This book provides an overview of the current state of pattern matching as seen by specialists who have devoted years of study to the field. It covers most of the basic principles and presents material advanced enough to faithfully portray the current frontier of research.

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Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications (repost)

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Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications (Studies in Fuzziness and Soft Computing)
Springer | April 15, 2008 | ISBN: 3540787364 | 248 pages | PDF | 3.56 MB

The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c-means become complete by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.

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Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (Repost)

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing By James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil Pal
Publisher: Springer 2005 | 776 Pages | ISBN: 0387245154 | PDF | 43 MB

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.

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Discrete Mathematics With Algorithms (Repost)

Discrete Mathematics With Algorithms
Publisher: John Wiley & Sons | ISBN: 0471849022 | edition 1988 | PDF | 560 pages | 12,8 mb

This first-year course in discrete mathematics requires no calculus or computer programming experience. The approach stresses finding efficient algorithms, rather than existential results. Provides an introduction to constructing proofs (especially by induction), and an introduction to algorithmic problem-solving. All algorithms are presented in English, in a format compatible with the Pascal programming language. Contains many exercises, with answers at the back of the book (detailed solutions being supplied for difficult problems).

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Foundations of Genetic Algorithms: 8th International Workshop, FOGA 2005, Japan 2005

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Foundations of Genetic Algorithms: 8th International Workshop, FOGA 2005, Aizu-Wakamatsu City, Japan, January 5-9, 2005, Revised Selected Papers (Lecture ... Computer Science and General Issues): Alden H. Wright, Michael D. Vose, Kenneth A. De Jong, Lothar M. Schmitt
Springer | ISBN: 3540272372 | 2005-08-22 | PDF (OCR) | 315 pages | 3.1 Mb

This book constitutes the refereed proceedings of the 8th workshop on the foundations of genetic algorithms, FOGA 2005, held in Aizu-Wakamatsu City, Japan, in January 2005. The 16 revised full papers presented provide an outstanding source of reference for the field of theoretical evolutionary computation including evolution strategies, evolutionary programming, and genetic programming, as well as the continuing growth in interactions with other fields such as mathematics, physics, and biology.

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Dynamical Systems, Graphs, and Algorithms (Lecture Notes in Mathematics): George Osipenko

Dynamical Systems, Graphs, and Algorithms (Lecture Notes in Mathematics): George Osipenko
Springer | ISBN: 3540355936 | 2006-11-16 | PDF (OCR) | 288 pages | 19.7 Mb

The modern theory and practice of dynamical systems requires the study of structures that fall outside the scope of traditional subjects of mathematical analysis. An important tool to investigate such complicated phenomena as chaos and strange attractors is the method of symbolic dynamics. This book describes a family of the algorithms to study global structure of systems.By a finite covering of the phase space we construct a directed graph (symbolic image) with vertices corresponding to cells of the covering and edges corresponding to admissible transitions.The method is used to localize the periodic orbits and the chain recurrent set, to construct the attractors and their basins, to estimate the entropy, Lyapunov exponents and the Morse spectrum, to verify the hyperbolicity and the structural stability.Considerable information can be obtained thus, and more techniques may be discovered in future research.

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Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications (repost)

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Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications (Studies in Fuzziness and Soft Computing)
Springer | April 15, 2008 | ISBN: 3540787364 | 248 pages | PDF | 3.56 MB

The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c-means become complete by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.

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An Introduction to the Analysis of Algorithms

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An Introduction to the Analysis of Algorithms By Robert Sedgewick, Philippe Flajolet
Publisher: Addison-Wesley Professional 1995 | 512 Pages | ISBN: 020140009X | DJVU | 10 MB

This book is intended to be a thorough overview of the primary techniques used in the mathematical analysis of algorithms. The material covered draws from classical mathematical topics, including discrete mathematics, elementary real analysis, and combinatorics; as well as from classical computer science topics, including algorithms and data structures. The focus is on "average-case'' or "probabilistic'' analysis, though the basic mathematical tools required for "worst-case" or "complexity" analysis are covered, as well.
It is assumed that the reader has some familiarity with basic concepts in both computer science and real analysis. In a nutshell, the reader should be able to both write programs and prove theorems; otherwise, the book is intended to be self-contained. Ample references to preparatory material in the literature are also provided. A planned companion volume will cover more advanced techniques. Together, the books are intended to cover the main techniques and to provide access to the growing research literature on the analysis of algorithms.
The book is meant to be used as a textbook in a junior- or senior-level course on "Mathematical Analysis of Algorithms.'' It might also be useful in a course in discrete mathematics for computer scientists, since it covers basic techniques in discrete mathematics as well as combinatorics and basic properties of important discrete structures within a familiar context for computer science students. It is traditional to have somewhat broader coverage in such courses, but many instructors may find the approach here a useful way to engage students in a substantial portion of the material. The book also can be used to introduce students in mathematics and applied mathematics to principles from computer science related to algorithms and data structures.
Supplemented by papers from the literature, the book can serve as the basis for an introductory graduate course on the analysis of algorithms, or as a reference or basis for self-study by researchers in mathematics or computer science who want access to the literature in this field. It also might be of use to students and researchers in combinatorics and discrete mathematics, as a source of applications and techniques.

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An Introduction to the Analysis of Algorithms

An Introduction to the Analysis of Algorithms By Robert Sedgewick, Philippe Flajolet
Publisher: Addison-Wesley Professional 1995 | 512 Pages | ISBN: 020140009X | DJVU | 10 MB

This book is intended to be a thorough overview of the primary techniques used in the mathematical analysis of algorithms. The material covered draws from classical mathematical topics, including discrete mathematics, elementary real analysis, and combinatorics; as well as from classical computer science topics, including algorithms and data structures. The focus is on "average-case'' or "probabilistic'' analysis, though the basic mathematical tools required for "worst-case" or "complexity" analysis are covered, as well.
It is assumed that the reader has some familiarity with basic concepts in both computer science and real analysis. In a nutshell, the reader should be able to both write programs and prove theorems; otherwise, the book is intended to be self-contained. Ample references to preparatory material in the literature are also provided. A planned companion volume will cover more advanced techniques. Together, the books are intended to cover the main techniques and to provide access to the growing research literature on the analysis of algorithms.
The book is meant to be used as a textbook in a junior- or senior-level course on "Mathematical Analysis of Algorithms.'' It might also be useful in a course in discrete mathematics for computer scientists, since it covers basic techniques in discrete mathematics as well as combinatorics and basic properties of important discrete structures within a familiar context for computer science students. It is traditional to have somewhat broader coverage in such courses, but many instructors may find the approach here a useful way to engage students in a substantial portion of the material. The book also can be used to introduce students in mathematics and applied mathematics to principles from computer science related to algorithms and data structures.
Supplemented by papers from the literature, the book can serve as the basis for an introductory graduate course on the analysis of algorithms, or as a reference or basis for self-study by researchers in mathematics or computer science who want access to the literature in this field. It also might be of use to students and researchers in combinatorics and discrete mathematics, as a source of applications and techniques.

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Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (Repost)

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing By James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil Pal
Publisher: Springer 2005 | 776 Pages | ISBN: 0387245154 | PDF | 43 MB

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.

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