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Application software. --- Machine learning. --- Signal processing --- Digital techniques. --- Digital signal processing --- Digital communications --- Digital electronics --- Learning, Machine --- Artificial intelligence --- Machine theory --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software
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This volume contains all the papers presented at the Ninth International Conference on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ¨aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Artificial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by artificial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses.
Computer algorithms --- Machine learning --- Congresses --- Computer science. --- Computers. --- Algorithms. --- Mathematical logic. --- Artificial intelligence. --- Computer Science. --- Theory of Computation. --- Artificial Intelligence (incl. Robotics). --- Mathematical Logic and Formal Languages. --- Algorithm Analysis and Problem Complexity. --- Information theory. --- Computer software. --- Artificial Intelligence. --- Software, Computer --- Computer systems --- Informatics --- Science --- 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 --- Communication theory --- Communication --- Cybernetics --- Algorism --- Algebra --- Arithmetic --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Mathematics --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Calculators --- Cyberspace --- Foundations --- Computer science --- Computer software --- Computer algorithms - Congresses --- Machine learning - Congresses
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While it is relatively easy to record billions of experiences in a database, the wisdom of a system is not measured by the number of its experiences but rather by its ability to make use of them. Case-based reasoning (CBR) can be viewed as experience mining, with analogical reasoning applied to problem–solution pairs. As cases are typically not identical, simple storage and recall of experiences is not sufficient, we must define and analyze similarity and adaptation. The fundamentals of the approach are now well-established, and there are many successful commercial applications in diverse fields, attracting interest from researchers across various disciplines. This textbook presents case-based reasoning in a systematic approach with two goals: to present rigorous and formally valid structures for precise reasoning, and to demonstrate the range of techniques, methods, and tools available for many applications. In the chapters in Part I the authors present the basic elements of CBR without assuming prior reader knowledge; Part II explains the core methods, in particular case representations, similarity topics, retrieval, adaptation, evaluation, revisions, learning, development, and maintenance; Part III offers advanced views of these topics, additionally covering uncertainty and probabilities; and Part IV shows the range of knowledge sources, with chapters on textual CBR, images, sensor data and speech, conversational CBR, and knowledge management. The book concludes with appendices that offer short descriptions of the basic formal definitions and methods, and comparisons between CBR and other techniques. The authors draw on years of teaching and training experience in academic and business environments, and they employ chapter summaries, background notes, and exercises throughout the book. It's suitable for advanced undergraduate and graduate students of computer science, management, and related disciplines, and it's also a practical introduction and guide for industrial researchers and practitioners engaged with knowledge engineering systems.
Engineering & Applied Sciences --- Mechanical Engineering --- Computer Science --- Mechanical Engineering - General --- Computer science. --- Information technology. --- Business --- Computers. --- Artificial intelligence. --- Application software. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- IT in Business. --- Information Systems and Communication Service. --- Computer Applications. --- Data processing. --- Case-based reasoning. --- Case-based learning --- Reasoning --- 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 --- Information systems. --- Artificial Intelligence. --- Informatics --- Science --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Business—Data processing. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace
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Mathematical logic --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- bedrijfseconomie --- wiskunde --- KI (kunstmatige intelligentie) --- informatica management --- logica --- robots
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While it is relatively easy to record billions of experiences in a database, the wisdom of a system is not measured by the number of its experiences but rather by its ability to make use of them. Case-based reasoning (CBR) can be viewed as experience mining, with analogical reasoning applied to problem–solution pairs. As cases are typically not identical, simple storage and recall of experiences is not sufficient, we must define and analyze similarity and adaptation. The fundamentals of the approach are now well-established, and there are many successful commercial applications in diverse fields, attracting interest from researchers across various disciplines. This textbook presents case-based reasoning in a systematic approach with two goals: to present rigorous and formally valid structures for precise reasoning, and to demonstrate the range of techniques, methods, and tools available for many applications. In the chapters in Part I the authors present the basic elements of CBR without assuming prior reader knowledge; Part II explains the core methods, in particular case representations, similarity topics, retrieval, adaptation, evaluation, revisions, learning, development, and maintenance; Part III offers advanced views of these topics, additionally covering uncertainty and probabilities; and Part IV shows the range of knowledge sources, with chapters on textual CBR, images, sensor data and speech, conversational CBR, and knowledge management. The book concludes with appendices that offer short descriptions of the basic formal definitions and methods, and comparisons between CBR and other techniques. The authors draw on years of teaching and training experience in academic and business environments, and they employ chapter summaries, background notes, and exercises throughout the book. It's suitable for advanced undergraduate and graduate students of computer science, management, and related disciplines, and it's also a practical introduction and guide for industrial researchers and practitioners engaged with knowledge engineering systems.
Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- ICT (informatie- en communicatietechnieken) --- bedrijfseconomie --- computers --- informatiesystemen --- KI (kunstmatige intelligentie) --- informatica management --- computerkunde --- robots
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While it is relatively easy to record billions of experiences in a database, the wisdom of a system is not measured by the number of its experiences but rather by its ability to make use of them. Case-based rea-soning (CBR) can be viewed as experience mining, with analogical reasoning applied to problem-solution pairs. As cases are typically not identical, simple storage and recall of experiences is not sufficient, we must define and analyze similarity and adaptation. The fundamentals of the approach are now well-established, and there are many successful commercial applications in diverse fields, attracting interest from researchers across various disciplines. This textbook presents case-based reasoning in a systematic approach with two goals: to present rigorous and formally valid structures for precise reasoning, and to demonstrate the range of techniques, methods, and tools available for many applications. In the chapters in Part I the authors present the basic elements of CBR without assuming prior reader knowledge; Part II explains the core methods, in particu-lar case representations, similarity topics, retrieval, adaptation, evaluation, revisions, learning, develop-ment, and maintenance; Part III offers advanced views of these topics, additionally covering uncertainty and probabilities; and Part IV shows the range of knowledge sources, with chapters on textual CBR, im-ages, sensor data and speech, conversational CBR, and knowledge management. The book concludes with appendices that offer short descriptions of the basic formal definitions and methods, and comparisons be-tween CBR and other techniques. The authors draw on years of teaching and training experience in academic and business environments, and they employ chapter summaries, background notes, and exercises throughout the book. It's suitable for advanced undergraduate and graduate students of computer science, management, and related disciplines, and it's also a practical introduction and guide for industrial researchers and practitioners engaged with knowledge engineering systems.
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This volume presents the 36 full refereed papers selected from the presentations at the First European Workshop on Case-Based Reasoning, held at Kaiserslautern, Germany in November 1993. Case-based reasoning (CBR) has recently attracted much interest among AI researchers: it supports knowledge acquisition and problem solving, and it is related to machine learning, analogical reasoning, cognitive modeling, similarity, and information retrieval. EWCBR is now established as the prime European forum for CBR research. This volume reflects the importance of this dynamic area of research through essential contributions to all aspects of CBR research and advanced applications.
Expert systems (Computer science) --- Congresses --- Artificial intelligence. --- Information technology. --- Operations research. --- Artificial Intelligence. --- IT in Business. --- Operations Research/Decision Theory. --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- 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
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