marcus hutter (34 Ergebnisse)

Zustand: New. pp. 422 Illus.

hardcover. Zustand: Very Good.

Paperback. Zustand: Wie neu. XI, 402 S.; with figures, 24 cm, Like new. Shrink wrapped. / Wie neu. In Folie verschweißt. Sprache: Englisch Gewicht in Gramm: 710.

Paperback. Zustand: Brand New. 328 pages. German language. 8.11x5.79x0.71 inches. In Stock.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some wear and internal barcode may have been clipped, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

Zustand: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9783540221395.

Zustand: New.

Paperback. Zustand: Brand New. 2012 edition. 344 pages. 9.00x6.00x0.75 inches. In Stock.

Paperback. Zustand: Brand New. 1st edition. 402 pages. 9.00x6.00x1.00 inches. In Stock.

Zustand: New. 2012. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 434 pages. 9.20x6.10x1.00 inches. In Stock.

Zustand: New. Constitutes the refereed proceedings of the 21th International Conference on Algorithmic Learning Theory, ALT 2010, that was held in Canberra, Australia. Editor(s): Hutter, Marcus; Stephan, Frank; Vovk, Vladimir; Zeugmann, Thomas. Series: Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence. Num Pages: 421 pages, 45 black & white illustrations, biography. BIC Classification: JNV; UMB; UMX; UYQM. Category: (P) Professional & Vocational. Dimension: 231 x 155 x 25. Weight in Grams: 640. . 2010. Paperback. . . . . Books ship from the US and Ireland.

Zustand: NEW.

Zustand: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory (ALT 2007), which was held in Sendai (Japan) during October 1-4, 2007. The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scienti c interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference. The conference was co-located with the Tenth International Conference on Discovery Science (DS 2007). This volume includes 25 technical contributions that were selected from 50 submissions by the ProgramCommittee. It also contains descriptions of the ve invited talks of ALT and DS; longer versions of the DS papers are available in the proceedings of DS 2007. These invited talks were presented to the audience of both conferences in joint sessions.

Zustand: New. Marcus Hutter is Senior Researcher at DeepMind in London and Professor in the Research School of Computer Science (RSCS) at the Australian National University (ANU) in Canberra, Australia (fulltime till 2019 and honorary since then). He is Chair o.

Taschenbuch. Zustand: Neu. Neuware - This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6-8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decis iontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti c knowledge discovery. As is the tradition, it wasco-located and held in parallel with Algorithmic Learning Theory.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 360 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Zustand: New. 2010. Softcover reprint of hardcover 1st ed. 2005. paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware -Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 304 pp. Englisch.

Zustand: New. First book presenting machine learning/agent theory/sequential decision theory from an algorithmic information theory point of viewMarcus Hutter received his masters in computer sciences in 1992 at the Technical University in Munich, Germa.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 419 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Buch. Zustand: Neu. Neuware - Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.

Zustand: New.