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Author : Hanan Samet
Publisher : Morgan Kaufmann
ISBN 13 : 0123694469
Total Pages : 1023 pages
Book Rating : 4.1/5 (236 download)
Download or read book Foundations of Multidimensional and Metric Data Structures written by Hanan Samet and published by Morgan Kaufmann. This book was released on 2006-08-08 with total page 1023 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Author : Avrim Blum
Publisher : Cambridge University Press
ISBN 13 : 1108617360
Total Pages : 433 pages
Book Rating : 4.1/5 (86 download)
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Author : Hanan Samet
Publisher : Addison Wesley Publishing Company
ISBN 13 :
Total Pages : 536 pages
Book Rating : 4.3/5 (91 download)
Download or read book Applications of Spatial Data Structures written by Hanan Samet and published by Addison Wesley Publishing Company. This book was released on 1990 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Clifford A. Shaffer
Publisher : Courier Corporation
ISBN 13 : 0486173569
Total Pages : 607 pages
Book Rating : 4.4/5 (861 download)
Download or read book Data Structures and Algorithm Analysis in Java, Third Edition written by Clifford A. Shaffer and published by Courier Corporation. This book was released on 2012-09-06 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses Java as the programming language.
Author : Clifford A. Shaffer
Publisher :
ISBN 13 :
Total Pages : 536 pages
Book Rating : 4.:/5 (321 download)
Download or read book A Practical Introduction to Data Structures and Algorithm Analysis written by Clifford A. Shaffer and published by . This book was released on 2001 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical text contains fairly "traditional" coverage of data structures with a clear and complete use of algorithm analysis, and some emphasis on file processing techniques as relevant to modern programmers. It fully integrates OO programming with these topics, as part of the detailed presentation of OO programming itself.Chapter topics include lists, stacks, and queues; binary and general trees; graphs; file processing and external sorting; searching; indexing; and limits to computation.For programmers who need a good reference on data structures.
Author : Mark L. Gillenson
Publisher : Wiley
ISBN 13 : 9780470624708
Total Pages : 0 pages
Book Rating : 4.6/5 (247 download)
Download or read book Fundamentals of Database Management Systems written by Mark L. Gillenson and published by Wiley. This book was released on 2011-12-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gillenson's new edition of Fundamentals of Database Management Systems provides concise coverage of the fundamental topics necessary for a deep understanding of the basics. In this issue, there is more emphasis on a practical approach, with new "your turn" boxes and much more coverage in a separate supplement on how to implement databases with Access. In every chapter, the author covers concepts first, then show how they're implemented in continuing case(s.) "Your Turn" boxes appear several times throughout the chapter to apply concepts to projects. And "Concepts in Action" boxes contain examples of concepts used in practice. This pedagogy is easily demonstrable and the text also includes more hands-on exercises and projects and a standard diagramming style for the data modeling diagrams. Furthermore, revised and updated content and organization includes more coverage on database control issues, earlier coverage of SQL, and new coverage on data quality issues.
Author : Clifford A. Shaffer
Publisher : Courier Corporation
ISBN 13 : 0486172627
Total Pages : 626 pages
Book Rating : 4.4/5 (861 download)
Download or read book Data Structures and Algorithm Analysis in C++, Third Edition written by Clifford A. Shaffer and published by Courier Corporation. This book was released on 2012-07-26 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses C++ as the programming language.
Author : Pavel Zezula
Publisher : Springer Science & Business Media
ISBN 13 : 0387291512
Total Pages : 227 pages
Book Rating : 4.3/5 (872 download)
Download or read book Similarity Search written by Pavel Zezula and published by Springer Science & Business Media. This book was released on 2006-06-07 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: The area of similarity searching is a very hot topic for both research and c- mercial applications. Current data processing applications use data with c- siderably less structure and much less precise queries than traditional database systems. Examples are multimedia data like images or videos that offer query by example search, product catalogs that provide users with preference based search, scientific data records from observations or experimental analyses such as biochemical and medical data, or XML documents that come from hetero- neous data sources on the Web or in intranets and thus does not exhibit a global schema. Such data can neither be ordered in a canonical manner nor meani- fully searched by precise database queries that would return exact matches. This novel situation is what has given rise to similarity searching, also - ferred to as content based or similarity retrieval. The most general approach to similarity search, still allowing construction of index structures, is modeled in metric space. In this book. Prof. Zezula and his co authors provide the first monograph on this topic, describing its theoretical background as well as the practical search tools of this innovative technology.
Author : Steven S. Skiena
Publisher : Springer
ISBN 13 : 3319554441
Total Pages : 445 pages
Book Rating : 4.3/5 (195 download)
Download or read book The Data Science Design Manual written by Steven S. Skiena and published by Springer. This book was released on 2017-07-01 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
ISBN 13 : 1108569323
Total Pages : 392 pages
Book Rating : 4.1/5 (85 download)
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author : Louis-Marie Asselin
Publisher : Springer Science & Business Media
ISBN 13 : 1441908439
Total Pages : 212 pages
Book Rating : 4.4/5 (419 download)
Download or read book Analysis of Multidimensional Poverty written by Louis-Marie Asselin and published by Springer Science & Business Media. This book was released on 2009-08-29 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Poverty is a paradoxical state. Recognizable in the eld for any sensitive observer who travels in remote rural areas and urban slums and meets marginalized people in a given society, poverty still remains a challenge to conceptual formalization and to measurement that is consistent with such formalization. The analysis of poverty is multidisciplinary. It goes from ethics to economics, from political science to human biology, and any type of measurement rests on mathematics. Moreover, poverty is multifaceted according to the types of deprivation, and it is also gender and age speci c. A vector of variables is required, which raises a substantial problem for individual and group comparisons necessary to equity analysis. Multidimension- ity also complicates the aggregation necessary to perform the ef ciency analysis of policies. In the case of income poverty, these two problems, equity and ef ciency, have bene ted from very signi cant progress in the eld of economics. Similar achievements are still to come in the area of multidimensional poverty. Within this general background, this book has a very modest and narrow-scoped objective. It proposes an operational methodology for measuring multidimensional poverty, independent from the conceptual origin, the size and the qualitative as well as the quantitative nature of the primary indicators used to describe the poverty of an individual, a household or a sociodemographic entity.
Author : Ingwer Borg
Publisher : Springer Science & Business Media
ISBN 13 : 1475727119
Total Pages : 469 pages
Book Rating : 4.4/5 (757 download)
Download or read book Modern Multidimensional Scaling written by Ingwer Borg and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multidimensional scaling (MDS) is a technique for the analysis of similarity or dissimilarity data on a set of objects. Such data may be intercorrelations of test items, ratings of similarity on political candidates, or trade indices for a set of countries. MDS attempts to model such data as distances among points in a geometric space. The main reason for doing this is that one wants a graphical display of the structure of the data, one that is much easier to understand than an array of numbers and, moreover, one that displays the essential information in the data, smoothing out noise. There are numerous varieties of MDS. Some facets for distinguishing among them are the particular type of geometry into which one wants to map the data, the mapping function, the algorithms used to find an optimal data representation, the treatment of statistical error in the models, or the possibility to represent not just one but several similarity matrices at the same time. Other facets relate to the different purposes for which MDS has been used, to various ways of looking at or "interpreting" an MDS representation, or to differences in the data required for the particular models. In this book, we give a fairly comprehensive presentation of MDS. For the reader with applied interests only, the first six chapters of Part I should be sufficient. They explain the basic notions of ordinary MDS, with an emphasis on how MDS can be helpful in answering substantive questions.
Author : Franco P. Preparata
Publisher : Springer Science & Business Media
ISBN 13 : 1461210984
Total Pages : 413 pages
Book Rating : 4.4/5 (612 download)
Download or read book Computational Geometry written by Franco P. Preparata and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the reviews: "This book offers a coherent treatment, at the graduate textbook level, of the field that has come to be known in the last decade or so as computational geometry. ... ... The book is well organized and lucidly written; a timely contribution by two founders of the field. It clearly demonstrates that computational geometry in the plane is now a fairly well-understood branch of computer science and mathematics. It also points the way to the solution of the more challenging problems in dimensions higher than two." #Mathematical Reviews#1 "... This remarkable book is a comprehensive and systematic study on research results obtained especially in the last ten years. The very clear presentation concentrates on basic ideas, fundamental combinatorial structures, and crucial algorithmic techniques. The plenty of results is clever organized following these guidelines and within the framework of some detailed case studies. A large number of figures and examples also aid the understanding of the material. Therefore, it can be highly recommended as an early graduate text but it should prove also to be essential to researchers and professionals in applied fields of computer-aided design, computer graphics, and robotics." #Biometrical Journal#2
Author : Mark Allen Weiss
Publisher :
ISBN 13 : 9780321189967
Total Pages : 588 pages
Book Rating : 4.1/5 (899 download)
Download or read book Data Structures and Algorithm Analysis in C+ written by Mark Allen Weiss and published by . This book was released on 2003 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this second edition of his successful book, experienced teacher and author Mark Allen Weiss continues to refine and enhance his innovative approach to algorithms and data structures. Written for the advanced data structures course, this text highlights theoretical topics such as abstract data types and the efficiency of algorithms, as well as performance and running time. Before covering algorithms and data structures, the author provides a brief introduction to C++ for programmers unfamiliar with the language. Dr Weiss's clear writing style, logical organization of topics, and extensive use of figures and examples to demonstrate the successive stages of an algorithm make this an accessible, valuable text. New to this Edition *An appendix on the Standard Template Library (STL) *C++ code, tested on multiple platforms, that conforms to the ANSI ISO final draft standard 0201361221B04062001
Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)
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.
Author : Pieter Kubben
Publisher : Springer
ISBN 13 : 3319997130
Total Pages : 219 pages
Book Rating : 4.3/5 (199 download)
Download or read book Fundamentals of Clinical Data Science written by Pieter Kubben and published by Springer. This book was released on 2018-12-21 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Author : Roman Vershynin
Publisher : Cambridge University Press
ISBN 13 : 1108415199
Total Pages : 299 pages
Book Rating : 4.1/5 (84 download)
Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
