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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.

Computer science. --- Data mining. --- Artificial intelligence. --- Pattern recognition. --- Statistics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Data Mining and Knowledge Discovery. --- Statistics and Computing/Statistics Programs. --- Pattern Recognition. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Design perception --- Pattern recognition --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Informatics --- Mathematical statistics. --- Optical pattern recognition. --- Artificial Intelligence. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Database searching --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Machine learning --- Data mining --- Learning, Machine --- Artificial intelligence

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Statistical science --- Mathematical statistics --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- patroonherkenning --- factoranalyse --- datamining --- statistiek --- KI (kunstmatige intelligentie) --- data acquisition --- statistisch onderzoek

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Artificial intelligence. --- Data mining. --- Pattern recognition. --- Statistics . --- Artificial Intelligence. --- Data Mining and Knowledge Discovery. --- Pattern Recognition. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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This authoritative, expanded and updated third 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 1000 entries – over 200 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 Science include recent developments in Deep Learning, 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.

Artificial intelligence. --- Data mining. --- Statistics . --- Pattern recognition. --- Artificial Intelligence. --- Data Mining and Knowledge Discovery. --- Statistics and Computing/Statistics Programs. --- Pattern Recognition.

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AI 2004 was the seventeenth in the series of annual Australian arti?cial intel- gence conferences. This conference is the major forum for arti?cial intelligence research in Australia. It has consistently attracted strong international part- ipation. This year more than two thirds of the submissions were from outside Australia. The current volume is based on the proceedings of AI 2004. A total of 340 papers were submitted, which we believe to be a substantial increase on p- vious submission numbers to this series. A national and international program committee refereed full-length versions of all submitted papers. Each accepted paperwasreviewedbyatleastthreereviewers.Ofthese340submissions,78were accepted for oral presentation and a further 62 for poster presentation. This v- ume contains a regular paper of up to 12 pages length for each oral presentation and a short paper of up to 6 pages length for each poster presentation. In addition to the scienti?c track represented here, the conference featured an exciting program of tutorials and workshops, and plenary talks by four o- standinginvitedspeakers:MehranSahami(GoogleInc.andStanfordUniversity, USA), Michael J. Pazzani (National Science Foundation and University of Ca- fornia, Irvine, USA), Paul Compton (University of New South Wales, Australia) andAhChungTsoi(AustralianResearchCouncil,Australia).AI2004wascol- cated with Complex 2004, the 7th Asia-Paci?c Conference on Complex Systems, with the aim of promoting cross-fertilization and collaboration in areas of c- plex and intelligent systems.