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The theory of computation is based on concepts defined by logicians and mathematicians. Logic plays a fundamental role in computer science, and this book explains the basic theorems, as well as different techniques of proving them in classical and some non-classical logics. Important applications derived from concepts of logic for computer technology include Artificial Intelligence and Software Engineering. Providing an in-depth introduction to fundamental classical and non-classical logics, this textbook offers a comprehensive survey of logics for computer scientists. Logics for Computer Science contains intuitive introductory chapters explaining the need for logical investigations, motivations for different types of logics and some of their history. They are followed by strict formal approach chapters. All chapters contain many detailed examples explaining each of the introduced notions and definitions, well chosen sets of exercises with carefully written solutions, and sets of homework. Includes links to the author's companion lecture slides for each chapter: several hundred presentations which summarize the ideas presented in the chapters for ease of comprehension.
Logic design. --- Logic, Symbolic and mathematical. --- Artificial intelligence. --- Logics and Meanings of Programs. --- Mathematical Logic and Foundations. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Mathematics --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Design, Logic --- Design of logic systems --- Digital electronics --- Electronic circuit design --- Logic circuits --- Switching theory --- Computer logic. --- Mathematical logic. --- Computer science logic --- Logic, Symbolic and mathematical
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The theory of computation is based on concepts defined by logicians and mathematicians. Logic plays a fundamental role in computer science, and this book explains the basic theorems, as well as different techniques of proving them in classical and some non-classical logics. Important applications derived from concepts of logic for computer technology include Artificial Intelligence and Software Engineering. Providing an in-depth introduction to fundamental classical and non-classical logics, this textbook offers a comprehensive survey of logics for computer scientists. Logics for Computer Science contains intuitive introductory chapters explaining the need for logical investigations, motivations for different types of logics and some of their history. They are followed by strict formal approach chapters. All chapters contain many detailed examples explaining each of the introduced notions and definitions, well chosen sets of exercises with carefully written solutions, and sets of homework. Includes links to the author's companion lecture slides for each chapter: several hundred presentations which summarize the ideas presented in the chapters for ease of comprehension.
Mathematical logic --- Logic --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- ontwerpen --- programmeren (informatica) --- wiskunde --- KI (kunstmatige intelligentie) --- logica --- AI (artificiële intelligentie)
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This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigms and algorithms. The foundational studies contained in this book focus on a broad range of subjects, including conceptual framework of data mining, data preprocessing and data mining as generalization, probability theory perspective on fuzzy systems, rough set methodology on missing values, inexact multiple-grained causal complexes, complexity of the privacy problem, logical framework for template creation and information extraction, classes of association rules, pseudo statistical independence in a contingency table, and role of sample size and determinants in granularity of contingency matrix. The practical studies contained in this book cover different fields of data mining, including rule mining, classification, clustering, text mining, Web mining, data stream mining, time series analysis, privacy preservation mining, fuzzy data mining, ensemble approaches, and kernel based approaches. We believe that the works presented in this book will encourage the study of data mining as a scientific field and spark collaboration among researchers and practitioners.
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