Articles liés à Algorithmic Learning Theory: 15th International Conference,...
Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings - Couverture souple
Synopsis
Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning, LogicBasedLearning, andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 54,14
EUR 2,27 expédition vers Etats-Unis
Destinations, frais et délais
Résultats de recherche pour Algorithmic Learning Theory: 15th International Conference,...
Image d'archives
Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Lecture Notes in Computer Science)
Edité par
Springer, 2004
ISBN 10 : 3540233563
ISBN 13 : 9783540233565
Ancien ou d'occasion
Paperback
Vendeur : GuthrieBooks, Spring Branch, TX, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : Very Good. We are unable to ship to Canada at this time.Ex-library paperback in very nice condition with the usual markings and attachments. Except for library markings, interior clean and unmarked. Tight binding. N° de réf. du vendeur UTD1422823
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Algorithmic Learning Theory : 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 3195666-n
Quantité disponible : Plus de 20 disponibles
Image d'archives
Algorithmic Learning Theory (Paperback)
Edité par
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2004
ISBN 10 : 3540233563
ISBN 13 : 9783540233565
Neuf
Paperback
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. This book constitutes the refereed proceedings of the 15th International Conference on Algorithmic Learning Theory, ALT 2004, held in Padova, Italy in October 2004. The 29 revised full papers presented together with 5 invited papers and 3 tutorial summaries were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on inductive inference, PAC learning and boosting, statistical supervised learning, online sequence learning, approximate optimization algorithms, logic based learning, and query and reinforcement learning. Algorithmic learning theory is mathematics about computer programs which learn from experience. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783540233565
Quantité disponible : 1 disponible(s)
Image d'archives
Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Lecture Notes in Computer Science, 3244)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur ABLIING23Mar3113020163608
Quantité disponible : Plus de 20 disponibles
Image d'archives
Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Lecture Notes in Computer Science, 3244)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9783540233565_new
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Algorithmic Learning Theory : 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 3195666-n
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Algorithmic Learning Theory
Edité par
Springer Berlin Heidelberg Sep 2004, 2004
ISBN 10 : 3540233563
ISBN 13 : 9783540233565
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the eld, placing each of these topics in the general context of the eld. Formal models of automated learning re ect various facets of the wide range of activities that can be viewed as learning. A rst dichotomy is between viewing learning as an inde nite process and viewing it as a nite activity with a de ned termination. Inductive Inference models focus on inde nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion. 528 pp. Englisch. N° de réf. du vendeur 9783540233565
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Algorithmic Learning Theory
Edité par
Springer Berlin Heidelberg, 2004
ISBN 10 : 3540233563
ISBN 13 : 9783540233565
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also con. N° de réf. du vendeur 4885875
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Algorithmic Learning Theory
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2004, 2004
ISBN 10 : 3540233563
ISBN 13 : 9783540233565
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the eld, placing each of these topics in the general context of the eld. Formal models of automated learning re ect various facets of the wide range of activities that can be viewed as learning. A rst dichotomy is between viewing learning as an inde nite process and viewing it as a nite activity with a de ned termination. Inductive Inference models focus on inde nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion. 528 pp. Englisch. N° de réf. du vendeur 9783540233565
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Algorithmic Learning Theory : 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings
Edité par
Springer Berlin Heidelberg, 2004
ISBN 10 : 3540233563
ISBN 13 : 9783540233565
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the eld, placing each of these topics in the general context of the eld. Formal models of automated learning re ect various facets of the wide range of activities that can be viewed as learning. A rst dichotomy is between viewing learning as an inde nite process and viewing it as a nite activity with a de ned termination. Inductive Inference models focus on inde nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion. N° de réf. du vendeur 9783540233565
Quantité disponible : 1 disponible(s)