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This book is a state-of-the-art introduction into both algorithmic techniques for fixed-parameter tractability and the structural theory of parameterized complexity classes. It presents detailed proofs of recent advanced results that have not appeared in book form before and replaces the earlier publication "Parameterized Complexity" by Downey and Fellows as the definitive book on this subject. The book will interest computer scientists, mathematicians and graduate students engaged with algorithms and problem complexity.
Book Synopsis Parameterized Complexity Theory by : J. Flum
Download or read book Parameterized Complexity Theory written by J. Flum and published by Springer Science & Business Media. This book was released on 2006-05-01 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a state-of-the-art introduction into both algorithmic techniques for fixed-parameter tractability and the structural theory of parameterized complexity classes. It presents detailed proofs of recent advanced results that have not appeared in book form before and replaces the earlier publication "Parameterized Complexity" by Downey and Fellows as the definitive book on this subject. The book will interest computer scientists, mathematicians and graduate students engaged with algorithms and problem complexity.
This book constitutes the proceedings of the 5th Conference on Computability in Europe, CiE 2009, held in Heidelberg, Germany, during July 19-24, 2009. The 34 papers presented together with 17 invited lectures were carefully reviewed and selected from 100 submissions. The aims of the conference is to advance our theoretical understanding of what can and cannot be computed, by any means of computation. It is the largest international meeting focused on computability theoretic issues.
Book Synopsis Mathematical Theory and Computational Practice by : Klaus Ambos-Spies
Download or read book Mathematical Theory and Computational Practice written by Klaus Ambos-Spies and published by Springer Science & Business Media. This book was released on 2009-07-15 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 5th Conference on Computability in Europe, CiE 2009, held in Heidelberg, Germany, during July 19-24, 2009. The 34 papers presented together with 17 invited lectures were carefully reviewed and selected from 100 submissions. The aims of the conference is to advance our theoretical understanding of what can and cannot be computed, by any means of computation. It is the largest international meeting focused on computability theoretic issues.
This Festschrift was published in honor of Hans L. Bodlaender on the occasion of his 60th birthday. The 14 full and 5 short contributions included in this volume show the many transformative discoveries made by H.L. Bodlaender in the areas of graph algorithms, parameterized complexity, kernelization and combinatorial games. The papers are written by his former Ph.D. students and colleagues as well as by his former Ph.D. advisor, Jan van Leeuwen. Chapter “Crossing Paths with Hans Bodlaender: A Personal View on Cross-Composition for Sparsification Lower Bounds” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Book Synopsis Treewidth, Kernels, and Algorithms by : Fedor V. Fomin
Download or read book Treewidth, Kernels, and Algorithms written by Fedor V. Fomin and published by Springer Nature. This book was released on 2020-04-20 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Festschrift was published in honor of Hans L. Bodlaender on the occasion of his 60th birthday. The 14 full and 5 short contributions included in this volume show the many transformative discoveries made by H.L. Bodlaender in the areas of graph algorithms, parameterized complexity, kernelization and combinatorial games. The papers are written by his former Ph.D. students and colleagues as well as by his former Ph.D. advisor, Jan van Leeuwen. Chapter “Crossing Paths with Hans Bodlaender: A Personal View on Cross-Composition for Sparsification Lower Bounds” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
This book constitutes the proceedings of the 17th International Computer Science Symposium in Russia, CSR 2022, held in St. Petersburg, Russia, June 29-July 3, 2022. The 21 full papers were carefully reviewed and selected from 51 submissions. The papers cover a broad range of topics, such as formal languages and automata theory, geometry and discrete structures; theory and algorithms for application domains and much more.
Book Synopsis Computer Science – Theory and Applications by : Alexander S. Kulikov
Download or read book Computer Science – Theory and Applications written by Alexander S. Kulikov and published by Springer Nature. This book was released on 2022-06-23 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 17th International Computer Science Symposium in Russia, CSR 2022, held in St. Petersburg, Russia, June 29-July 3, 2022. The 21 full papers were carefully reviewed and selected from 51 submissions. The papers cover a broad range of topics, such as formal languages and automata theory, geometry and discrete structures; theory and algorithms for application domains and much more.
This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006. The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions.
Book Synopsis Ant Colony Optimization and Swarm Intelligence by : Marco Dorigo
Download or read book Ant Colony Optimization and Swarm Intelligence written by Marco Dorigo and published by Springer Science & Business Media. This book was released on 2006-08-30 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006. The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions.
Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms. The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader’s comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.
Book Synopsis Elements of Dimensionality Reduction and Manifold Learning by : Benyamin Ghojogh
Download or read book Elements of Dimensionality Reduction and Manifold Learning written by Benyamin Ghojogh and published by Springer Nature. This book was released on 2023-02-02 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms. The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader’s comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.
This book constitutes the refereed proceedings of the 14th Algorithms and Data Structures Symposium, WADS 2015, held in Victoria, BC, Canada, August 2015. The 54 revised full papers presented in this volume were carefully reviewed and selected from 148 submissions. The Algorithms and Data Structures Symposium - WADS (formerly Workshop on Algorithms And Data Structures), which alternates with the Scandinavian Workshop on Algorithm Theory, is intended as a forum for researchers in the area of design and analysis of algorithms and data structures. WADS includes papers presenting original research on algorithms and data structures in all areas, including bioinformatics, combinatorics, computational geometry, databases, graphics, and parallel and distributed computing.
Book Synopsis Algorithms and Data Structures by : Frank Dehne
Download or read book Algorithms and Data Structures written by Frank Dehne and published by Springer. This book was released on 2015-07-27 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th Algorithms and Data Structures Symposium, WADS 2015, held in Victoria, BC, Canada, August 2015. The 54 revised full papers presented in this volume were carefully reviewed and selected from 148 submissions. The Algorithms and Data Structures Symposium - WADS (formerly Workshop on Algorithms And Data Structures), which alternates with the Scandinavian Workshop on Algorithm Theory, is intended as a forum for researchers in the area of design and analysis of algorithms and data structures. WADS includes papers presenting original research on algorithms and data structures in all areas, including bioinformatics, combinatorics, computational geometry, databases, graphics, and parallel and distributed computing.
Experimental algorithmics, as its name indicates, combines algorithmic work and experimentation: algorithms are not just designed, but also implemented and tested on a variety of instances. Perhaps the most important lesson in this process is that designing an algorithm is but the first step in the process of developing robust and efficient software for applications. Based on a seminar held at Dagstuhl Castle, Germany in September 2000, this state-of-the-art survey presents a coherent survey of the work done in the area so far. The 11 carefully reviewed chapters provide complete coverage of all current topics in experimental algorithmics.
Book Synopsis Experimental Algorithmics by : Rudolf Fleischer
Download or read book Experimental Algorithmics written by Rudolf Fleischer and published by Springer. This book was released on 2003-07-01 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental algorithmics, as its name indicates, combines algorithmic work and experimentation: algorithms are not just designed, but also implemented and tested on a variety of instances. Perhaps the most important lesson in this process is that designing an algorithm is but the first step in the process of developing robust and efficient software for applications. Based on a seminar held at Dagstuhl Castle, Germany in September 2000, this state-of-the-art survey presents a coherent survey of the work done in the area so far. The 11 carefully reviewed chapters provide complete coverage of all current topics in experimental algorithmics.
Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap. Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections: Part I: provides fundamentals of hyperspectral data processing Part II: offers various algorithm designs for endmember extraction Part III: derives theory for supervised linear spectral mixture analysis Part IV: designs unsupervised methods for hyperspectral image analysis Part V: explores new concepts on hyperspectral information compression Parts VI & VII: develops techniques for hyperspectral signal coding and characterization Part VIII: presents applications in multispectral imaging and magnetic resonance imaging Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages. Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.
Book Synopsis Hyperspectral Data Processing by : Chein-I Chang
Download or read book Hyperspectral Data Processing written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2013-04-08 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap. Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections: Part I: provides fundamentals of hyperspectral data processing Part II: offers various algorithm designs for endmember extraction Part III: derives theory for supervised linear spectral mixture analysis Part IV: designs unsupervised methods for hyperspectral image analysis Part V: explores new concepts on hyperspectral information compression Parts VI & VII: develops techniques for hyperspectral signal coding and characterization Part VIII: presents applications in multispectral imaging and magnetic resonance imaging Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages. Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.
This book constitutes the thoroughly refereed conference proceedings of the 38th International Symposium on Mathematical Foundations of Computer Science, MFCS 2013, held in Klosterneuburg, Austria, in August 2013. The 67 revised full papers presented together with six invited talks were carefully selected from 191 submissions. Topics covered include algorithmic game theory, algorithmic learning theory, algorithms and data structures, automata, formal languages, bioinformatics, complexity, computational geometry, computer-assisted reasoning, concurrency theory, databases and knowledge-based systems, foundations of computing, logic in computer science, models of computation, semantics and verification of programs, and theoretical issues in artificial intelligence.
Book Synopsis Mathematical Foundations of Computer Science 2013 by : Krishnendu Chatterjee
Download or read book Mathematical Foundations of Computer Science 2013 written by Krishnendu Chatterjee and published by Springer. This book was released on 2013-08-16 with total page 869 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed conference proceedings of the 38th International Symposium on Mathematical Foundations of Computer Science, MFCS 2013, held in Klosterneuburg, Austria, in August 2013. The 67 revised full papers presented together with six invited talks were carefully selected from 191 submissions. Topics covered include algorithmic game theory, algorithmic learning theory, algorithms and data structures, automata, formal languages, bioinformatics, complexity, computational geometry, computer-assisted reasoning, concurrency theory, databases and knowledge-based systems, foundations of computing, logic in computer science, models of computation, semantics and verification of programs, and theoretical issues in artificial intelligence.
