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This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.
Computer algorithms --- Machine learning --- Information Technology --- Artificial Intelligence --- 681.3*F4 <063> --- 681.3*I26 <063> --- 681.3*I7 <063> --- 681.3*K3 <063> --- 681.3*I26 <063> Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32}--Congressen --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32}--Congressen --- Mathematical logic and formal languages (Theory of computation)--Congressen --- Text processing (Computing methodologies)--See also {681.3*H4}--Congressen --- Computers and education--Congressen --- Data mining. --- Artificial intelligence. --- Natural language processing (Computer science). --- Information systems. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence. --- Natural Language Processing (NLP). --- Computer Appl. in Arts and Humanities. --- NLP (Computer science) --- Artificial intelligence --- Electronic data processing --- Human-computer interaction --- Semantic computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software
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Informatique --- Ordinateurs --- Information Technology --- Computer Science (Hardware & Networks) --- Informatique - Congrès --- Ordinateurs - Congrès
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Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
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This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.
Human sciences (algemeen) --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- Translation science --- Linguistics --- informatica --- sociale wetenschappen --- vertalen --- linguïstiek --- database management --- KI (kunstmatige intelligentie) --- robots
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Human sciences (algemeen) --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- Translation science --- Linguistics --- informatica --- sociale wetenschappen --- vertalen --- linguïstiek --- database management --- KI (kunstmatige intelligentie) --- robots
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