Machine learning von mitchell tom (26 Ergebnisse)

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Hardcover. Zustand: Bon. Ancien livre de bibliothèque avec équipements. Edition 1986. Tome 2. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Edition 1986. Volume 2. Ammareal gives back up to 15% of this item's net price to charity organizations.…

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24 x 15,5 cm. Zustand: Gut. 1. Auflage. X, 1 Blatt, 738 Seiten Innen sauberer, guter Zustand. Hardcover, Pappeinband, mit den üblichen Bibliotheks-Markierungen, Stempeln und Einträgen, innen wie außen, siehe Bilder. Ecken und Kanten etwas berieben und bestoßen. Oberes Kapital bestoßen. - An Artificial Intelligence Approach, Volume II. B07-02-06Y Sprache: Englisch Gewicht in Gramm: 1170.…

Broschiert. Zustand: Gut. International Edition. 414 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 550 International Edition.…

**Pp. Zustand: Gut. 2. Aufl. 738 Seiten , 1170 g - gutes Exemplar, Einband etwas angestoßen, innen sehr gut (Vorsätze unregelmäßig u. leicht gedunkelt, unkenntlich gemachter Namensstempel (siehe auch mein Foto) [sw.] --- Good condition with normal signs of use gra Sprache: Englisch Gewicht in Gramm: 1170. …

Pappband. 1983. IX, 572 S. Opp. Buch.

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Zustand: New. In English.

Zustand: New. In English.

Gebunden. Zustand: New. Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning .

Paperback. Zustand: Brand New. reprint edition. 224 pages. 9.13x6.10x0.59 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).…

Buch. Zustand: Neu. Neuware - Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).…

Zustand: New. In English.

Zustand: New. In English.

Paperback. Zustand: Brand New. reprint edition. 429 pages. 9.25x6.00x1.00 inches. In Stock.

Zustand: New. Editor(s): Mitchell, T. M.; Carbonell, Jaime G.; Michalski, Ryszard S. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 429 pages, biography. BIC Classification: UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 254 x 178 x 25. Weight in Grams: 991. . 1986. Hardback. . . . . Books ship from the US and Ireland. …

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.…