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Book
Algorithmic learning theory : 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings
Authors: ---
ISSN: 03029743 ISBN: 3642341055 3642341063 Year: 2012 Publisher: Berlin ; New York : Springer,

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Abstract

This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.

Keywords

Computer algorithms --- Machine learning --- Mechanical Engineering --- Engineering & Applied Sciences --- Computer Science --- Mechanical Engineering - General --- Information Technology --- Artificial Intelligence --- Computer science. --- Computers. --- Algorithms. --- Computer logic. --- Mathematical logic. --- Artificial intelligence. --- Pattern recognition. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Mathematical Logic and Formal Languages. --- Algorithm Analysis and Problem Complexity. --- Computation by Abstract Devices. --- Logics and Meanings of Programs. --- Pattern Recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- 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 --- Computer science logic --- Logic, Symbolic and mathematical --- Algorism --- Algebra --- Arithmetic --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Informatics --- Science --- Foundations --- Computer software. --- Logic design. --- Optical pattern recognition. --- Artificial Intelligence. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Design, Logic --- Design of logic systems --- Digital electronics --- Electronic circuit design --- Logic circuits --- Switching theory --- Software, Computer


Digital
Learning Theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA; June 13-15, 2007. Proceedings
Authors: ---
ISBN: 9783540729273 Year: 2007 Publisher: Berlin, Heidelberg Springer-Verlag Berlin Heidelberg

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Learning theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 : proceedings
Authors: --- ---
ISBN: 9783540729259 3540729259 9786610940783 1280940786 3540729275 Year: 2007 Publisher: Berlin, Germany : Springer,

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Keywords

Computational learning theory --- Artificial intelligence --- Machine learning --- Apprentissage informatique, Théorie de l' --- Intelligence artificielle --- Apprentissage automatique --- Congresses. --- Congrès --- Machine learning -- Congresses. --- Engineering & Applied Sciences --- Computer Science --- Congresses --- Théorie de l'apprentissage informatique --- Computer science. --- Computers. --- Algorithms. --- Mathematical logic. --- Artificial intelligence. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Computation by Abstract Devices. --- Algorithm Analysis and Problem Complexity. --- Mathematical Logic and Formal Languages. --- 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 --- Algorism --- Algebra --- Arithmetic --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Informatics --- Science --- Foundations --- Computer software. --- Artificial Intelligence. --- Software, Computer


Book
Learning Theory
Authors: --- ---
ISBN: 9783540729273 Year: 2007 Publisher: Berlin, Heidelberg Springer-Verlag Berlin Heidelberg

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Abstract

This volumecontains paperspresentedatthe 20thAnnualConferenceonLea- ing Theory (previously known as the Conference on Computational Learning Theory) held in San Diego, USA, June 13-15, 2007, as part of the 2007 Fed- ated Computing Research Conference (FCRC). The Technical Program contained 41 papers selected from 92 submissions, 5 open problems selected from among 7 contributed, and 2 invited lectures. The invited lectures were givenby Dana Ron on PropertyTesting: A Learning T- oryPerspective, andbySantoshVempalaonSpectralAlgorithmsforLearning and Clustering.  The abstracts of these lectures are included in this volume. The Mark Fulk Award is presented annually for the best paper co-authored by a student. The student selected this year was Samuel E. Moelius III for the paper U-Shaped, Iterative, and Iterative-with-Counter Learning  co-authored with John Case. This year, student awards were also granted by the Machine LearningJournal.Wehavethereforebeenabletoselecttwomorestudentpapers forprizes.Thestudents selectedwereLev Reyzinforthe paperLearningLarge- Alphabet and Analog Circuits with Value Injection Queries  (co-authored with Dana Angluin, James Aspnes, and Jiang Chen), and Jennifer Wortman for the paper Regret to the Best vs. Regret to the Average  (co-authored with Eyal Even-Dar, Michael Kearns, and Yishay Mansour). The selected papers cover a wide range of topics, including unsupervised, semisupervisedand activelearning,statistical learningtheory, regularizedlea- ing, kernel methods and SVM, inductive inference, learning algorithms and l- itations on learning, on-line and reinforcement learning. The last topic is part- ularly well represented, covering alone more than one-fourth of the total.


Book
Algorithmic Learning Theory
Authors: --- --- --- ---
ISBN: 9783642341069 9783642341052 Year: 2012 Publisher: Berlin, Heidelberg Springer Berlin Heidelberg

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Abstract

This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.

Keywords

Computer algorithms --- Machine learning --- 681.3*F1 --- 681.3*F2 --- 681.3*F41 --- 681.3*I23 --- 681.3*I26 --- 681.3*I2 --- 681.3*I23 Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence) --- Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence) --- 681.3*F1 Computation by abstract devices --- Computation by abstract devices --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI --- 681.3*F2 Analysis of algorithms and problem complexity--See also {681.3*B6}; {681.3*B7}; {681.3*F13} --- Analysis of algorithms and problem complexity--See also {681.3*B6}; {681.3*B7}; {681.3*F13} --- 681.3*F41 Mathematical logic: computability theory; computational logic; lambda calculus; logic programming; mechanical theorem proving; model theory; proof theory;recursive function theory--See also {681.3*F11}; {681.3*I22}; {681.3*I23} --- Mathematical logic: computability theory; computational logic; lambda calculus; logic programming; mechanical theorem proving; model theory; proof theory;recursive function theory--See also {681.3*F11}; {681.3*I22}; {681.3*I23} --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Conferences - Meetings

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