Encyclopedia of Algorithms (Print & Online; Springer) by
This dynamic reference work provides solutions to vital algorithmic problems for scholars, researchers, practitioners, teachers and students in fields such as computer science, mathematics, statistics, biology, economics, financial software, and medical informatics. This second edition is broadly expanded, building upon the success of its former edition with more than 450 new and updated entries. These entries are designed to ensure algorithms are presented from growing areas of research such as bioinformatics, combinatorial group testing, differential privacy, enumeration algorithms, game theory, massive data algorithms, modern learning theory, social networks, and VLSI CAD algorithms. Over 630 entries are organized alphabetically by problem, with subentries allowing for distinct solutions. Each entry includes a description of the basic algorithmic problem; the input and output specifications; key results; examples of applications; citations to key literature, open problems, experimental results, links to data sets and downloadable code. All entries are peer-reviewed, written by leading experts in the field--and each entry contains links to a summary of the author's research work. This defining reference is available in both print and online--a dynamic living work with hyperlinks to related entries, cross references citations, and a myriad other valuable URLs. New and Updated entries include: Algorithmic Aspects of Distributed Sensor Networks, Algorithms for Modern Computers Bioinformatics Certified Reconstruction and Mesh Generation Combinatorial Group Testing Compression of Text and Data Structures Computational Counting Computational Economics Computational Geometry Differential Privacy Enumeration Algorithms Exact Exponential Algorithms Game Theory Graph Drawing Group Testing Internet Algorithms Kernels and Compressions Massive Data Algorithms Mathematical Optimization Modern Learning Theory Social Networks Stable Marriage Problems, k-SAT Algorithms Sublinear Algorithms Tile Self-Assembly VLSI CAD Algorithms
Encyclopedia of Biometrics (Online; Springer) by
This encyclopedia provides a comprehensive reference to topics in biometrics including concepts, modalities, algorithms, devices, systems, security, performance testing, applications and standardization. With an A-Z format and over 1400 entries, it provides easy access to relevant information on all aspects of biometrics for those seeking entry into this broad field. Entries are written by experts in biometrics and related fields. Each entry includes a definition, key words, list of synonyms, list of related entries, illustration(s), applications and a bibliography. Most entries include useful literature references providing the reader with a portal to more detailed information. Comprehensive and tutorial, the Encyclopedia of Biometrics, 2nd Edition is a practical resource for experts in the field and professionals interested in aspects of biometrics.
Encyclopedia of Machine Learning and Data Mining (Online; Springer) by
This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic. Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature. The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.