Download or read book Spectral Algorithms written by Ravindran Kannan and published by Now Publishers Inc. This book was released on 2009 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral methods refer to the use of eigenvalues, eigenvectors, singular values and singular vectors. They are widely used in Engineering, Applied Mathematics and Statistics. More recently, spectral methods have found numerous applications in Computer Science to "discrete" as well as "continuous" problems. Spectral Algorithms describes modern applications of spectral methods, and novel algorithms for estimating spectral parameters. The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations. A feature of many modern applications is the massive amount of input data. While sophisticated algorithms for matrix computations have been developed over a century, a more recent development is algorithms based on "sampling on the fly" from massive matrices. Good estimates of singular values and low rank approximations of the whole matrix can be provably derived from a sample. The main emphasis in the second part of the book is to present these sampling methods with rigorous error bounds. It also presents recent extensions of spectral methods from matrices to tensors and their applications to some combinatorial optimization problems.
Download or read book Parallel Numerical Algorithms written by David E. Keyes and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this volume, designed for computational scientists and engineers working on applications requiring the memories and processing rates of large-scale parallelism, leading algorithmicists survey their own field-defining contributions, together with enough historical and bibliographical perspective to permit working one's way to the frontiers. This book is distinguished from earlier surveys in parallel numerical algorithms by its extension of coverage beyond core linear algebraic methods into tools more directly associated with partial differential and integral equations - though still with an appealing generality - and by its focus on practical medium-granularity parallelism, approachable through traditional programming languages. Several of the authors used their invitation to participate as a chance to stand back and create a unified overview, which nonspecialists will appreciate.
Download or read book Algorithm Engineering written by Lasse Kliemann and published by Springer. This book was released on 2016-11-10 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithm Engineering is a methodology for algorithmic research that combines theory with implementation and experimentation in order to obtain better algorithms with high practical impact. Traditionally, the study of algorithms was dominated by mathematical (worst-case) analysis. In Algorithm Engineering, algorithms are also implemented and experiments conducted in a systematic way, sometimes resembling the experimentation processes known from fields such as biology, chemistry, or physics. This helps in counteracting an otherwise growing gap between theory and practice.
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Download or read book Proceedings of the Sixth Annual ACM SIAM Symposium on Discrete Algorithms written by and published by SIAM. This book was released on 1995-01-01 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings of the January 1995 symposium, sponsored by the ACM Special Interest Group on Algorithms and Computation Theory and the SIAM Activity Group on Discrete Mathematics, comprise 70 papers. Among the topics: on-line approximate list indexing with applications; finding subsets maximizing minimum structures; register allocation in structured programs; and splay trees for data compression. No index. Annotation copyright by Book News, Inc., Portland, OR
Download or read book Spectra of Graphs written by Andries E. Brouwer and published by Springer Science & Business Media. This book was released on 2011-12-17 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives an elementary treatment of the basic material about graph spectra, both for ordinary, and Laplace and Seidel spectra. The text progresses systematically, by covering standard topics before presenting some new material on trees, strongly regular graphs, two-graphs, association schemes, p-ranks of configurations and similar topics. Exercises at the end of each chapter provide practice and vary from easy yet interesting applications of the treated theory, to little excursions into related topics. Tables, references at the end of the book, an author and subject index enrich the text. Spectra of Graphs is written for researchers, teachers and graduate students interested in graph spectra. The reader is assumed to be familiar with basic linear algebra and eigenvalues, although some more advanced topics in linear algebra, like the Perron-Frobenius theorem and eigenvalue interlacing are included.
Download or read book Computing and Combinatorics written by Tandy Warnow and published by Springer Science & Business Media. This book was released on 2003-07-09 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume were presented at the 9th Annual International C- puting and Combinatorics Conference (COCOON 2003), held July 25–28, 2003, in Big Sky, MT, USA. The topics cover most aspects of theoretical computer science and combinatorics related to computing. Submissionstotheconferencethisyearwereconductedelectronically.Atotal of 114 papers were submitted, of which 52 were accepted. The papers were evaluated by an international program committee consisting of Nina Amenta, Tetsuo Asano, Bernard Chazelle, Zhixiang Chen, Francis Chin, Kyung-Yong Chwa, Robert Cimikowski, Anne Condon, Michael Fellows, Anna Gal, Michael Hallett,DanielHuson,NaokiKatoh,D.T.Lee,BernardMoret,BrendanMumey, Gene Myers, Hung Quang Ngo, Takao Nishizeki, Cindy Phillips, David Sanko?, Denbigh Starkey, Jie Wang, Lusheng Wang, Tandy Warnow and Binhai Zhu. It is expected that most of the accepted papers will appear in a more complete form in scienti?c journals. The submitted papers were from Canada (6), China (7), Estonia (1), F- land (1), France (1), Germany (8), Israel (4), Italy (1), Japan (11), Korea (22), Kuwait (1), New Zealand (1), Singapore (2), Spain (1), Sweden (2), Switzerland (3), Taiwan (7), the UK (1) and the USA (34). Each paper was evaluated by at least three Program Committee members, assisted in some cases by subre- rees. In addition to selected papers, the conference also included three invited presentations by Jon Bentley, Dan Gus?eld and Joel Spencer.
Download or read book ITNG 2024 21st International Conference on Information Technology New Generations written by Shahram Latifi and published by Springer Nature. This book was released on with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computing and Combinatorics written by Kyung-Yong Chwa and published by Springer Science & Business Media. This book was released on 2004-08-04 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Annual International Computing and Combinatorics Conference, COCOON 2004, held in Jeju Island, Korea, in August 2004. The 46 revised full papers presented together with abstracts of 3 invited talks were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on data structures and algorithms, computational geometry, games and combinatorics, combinatorial optimization, graph algorithms, automata and learning theory, scheduling, graph drawing, complexity theory, parallel and distributed architectures, and computational biology.
Download or read book Graph Theory Approximation Methods written by N.B. Singh and published by N.B. Singh. This book was released on with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the fascinating realm of graph theory through the lens of approximation methods in this comprehensive guide, Graph Theory: Approximation Methods . From fundamental concepts to advanced algorithms, this book delves into strategies for solving complex optimization problems in networks, offering insights and techniques essential for both students and researchers in the field. Discover practical applications, theoretical foundations, and cutting-edge developments that shape the future of graph theory and its computational applications.
Download or read book Machine Learning for Beginners written by Dr. Harsh Bhasin and published by BPB Publications. This book was released on 2023-10-16 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build a complete machine learning pipeline by mastering feature extraction, feature selection, and algorithm training KEY FEATURES ● Develop a solid understanding of foundational principles in machine learning. ● Master regression and classification methods for accurate data prediction and categorization in machine learning. ● Dive into advanced machine learning topics, including unsupervised learning and deep learning. DESCRIPTION The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in machine learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection, providing comprehensive coverage of various techniques such as the Fourier transform, short-time Fourier transform, and local binary patterns. Moving on, the book discusses principal component analysis and linear discriminant analysis. Next, the book covers the topics of model representation, training, testing, and cross-validation. It emphasizes regression and classification, explaining and implementing methods such as gradient descent. Essential classification techniques, including k-nearest neighbors, logistic regression, and naive Bayes, are also discussed in detail. The book then presents an overview of neural networks, including their biological background, the limitations of the perceptron, and the backpropagation model. It also covers support vector machines and kernel methods. Decision trees and ensemble models are also discussed. The final section of the book provides insight into unsupervised learning and deep learning, offering readers a comprehensive overview of these advanced topics. By the end of the book, you will be well-prepared to explore and apply machine learning in various real-world scenarios. WHAT YOU WILL LEARN ● Acquire skills to effectively prepare data for machine learning tasks. ● Learn how to implement learning algorithms from scratch. ● Harness the power of scikit-learn to efficiently implement common algorithms. ● Get familiar with various Feature Selection and Feature Extraction methods. ● Learn how to implement clustering algorithms. WHO THIS BOOK IS FOR This book is for both undergraduate and postgraduate Computer Science students as well as professionals looking to transition into the captivating realm of Machine Learning, assuming a foundational familiarity with Python. TABLE OF CONTENTS Section I: Fundamentals 1. An Introduction to Machine Learning 2. The Beginning: Data Pre-Processing 3. Feature Selection 4. Feature Extraction 5. Model Development Section II: Supervised Learning 6. Regression 7. K-Nearest Neighbors 8. Classification: Logistic Regression and Naïve Bayes Classifier 9. Neural Network I: The Perceptron 10. Neural Network II: The Multi-Layer Perceptron 11. Support Vector Machines 12. Decision Trees 13. An Introduction to Ensemble Learning Section III: Unsupervised Learning and Deep Learning 14. Clustering 15. Deep Learning Appendix 1: Glossary Appendix 2: Methods/Techniques Appendix 3: Important Metrics and Formulas Appendix 4: Visualization- Matplotlib Answers to Multiple Choice Questions Bibliography
Download or read book Combinatorial Algorithms for Integrated Circuit Layout written by and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last decade has brought explosive growth in the technology for manufac turing integrated circuits. Integrated circuits with several hundred thousand transistors are now commonplace. This manufacturing capability, combined with the economic benefits of large electronic systems, is forcing a revolution in the design of these systems and providing a challenge to those people in terested in integrated system design. Modern circuits are too complex for an individual to comprehend completely. Managing tremendous complexity and automating the design process have become crucial issues. Two groups are interested in dealing with complexity and in developing algorithms to automate the design process. One group is composed of practi tioners in computer-aided design (CAD) who develop computer programs to aid the circuit-design process. The second group is made up of computer scientists and mathemati'::~l\ns who are interested in the design and analysis of efficient combinatorial aJ::,orithms. These two groups have developed separate bodies of literature and, until recently, have had relatively little interaction. An obstacle to bringing these two groups together is the lack of books that discuss issues of importance to both groups in the same context. There are many instances when a familiarity with the literature of the other group would be beneficial. Some practitioners could use known theoretical results to improve their "cut and try" heuristics. In other cases, theoreticians have published impractical or highly abstracted toy formulations, thinking that the latter are important for circuit layout.
Download or read book Sampling Techniques for Supervised or Unsupervised Tasks written by Frédéric Ros and published by Springer Nature. This book was released on 2019-10-26 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli
Download or read book Graph Theoretic Concepts in Computer Science written by Ulrik Brandes and published by Springer. This book was released on 2003-07-31 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 26th International Workshop on Graph-Theoretic Concepts in Computer Science (WG 2000) was held at Waldhaus Jakob, in Konstanz, Germany, on 15{ 17 June 2000. It was organized by the Algorithms and Data Structures Group of the Department of Computer and Information Science, University of K- stanz, and sponsored by Deutsche Forschungsgemeinschaft (DFG) and Univ- sit ̈atsgesellschaft Konstanz. The workshop aims at uniting theory and practice by demonstrating how graph-theoretic concepts can be applied to various areas in computer science, or by extracting new problems from applications. The goal is to present recent research results and to identify and explore directions for future research. The workshop looks back on a remarkable tradition of more than a quarter of a century. Previous Workshops have been organized in various places in Europe, and submissions come from all over the world. This year, 57 attendees from 13 di erent countries gathered in the relaxing atmosphere of Lake Constance, also known as the Bodensee. Out of 51 submis- ons, the program committee carefully selected 26 papers for presentation at the workshop. This selection re?ects current research directions, among them graph and network algorithms and their complexity, algorithms for special graph cl- ses, communication networks, and distributed algorithms. The present volume contains these papers together with the survey presented in an invited lecture by Ingo Wegener (University of Dortmund) and an extended abstract of the invited lecture given by Emo Welzl (ETH Zuric ̈ h).
Download or read book Information Hiding written by Ira S. Moskowitz and published by Springer. This book was released on 2003-06-30 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 4th International Information Hiding Workshop, IHW 2001, held in Pittsburgh, PA, USA, in April 2001. The 29 revised full papers presented were carefully selected during two rounds of reviewing and revision. All current issues in information hiding are addressed including watermarking and fingerprinting of digitial audio, still image and video; anonymous communications; steganography and subliminal channels; covert channels; and database inference channels.
Download or read book Proceedings of the Annual ACM SIAM Symposium on Discrete Algorithms written by and published by . This book was released on 1995 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings of the Fifth SIAM International Conference on Data Mining written by Hillol Kargupta and published by SIAM. This book was released on 2005-04-01 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fifth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. Advances in information technology and data collection methods have led to the availability of large data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines. The field of data mining draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high performance computing to discover interesting and previously unknown information in data. This conference results in data mining, including applications, algorithms, software, and systems.
