Learning Geometry Computational Approaches (12 results)
- Hardcover
Seller: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
- Hardcover
Seller: La bataille des livres, Pradinas, FranceLa bataille des livres
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£ 13.24
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Condition: Très bon. Learning and Geometry: Computational Approaches | Kueker, Smith | Birhaüser, 1996. In-8° cartonné, 210 pages. Couverture propre. Dos solide. Intérieur frais sans soulignage ou annotation. Exemplaire de bibliothèque : petit code barre en pied de 1re de couv., cotation au dos, rares et discrets petits tampons…à l'intérieur de l'ouvrage. Très bon état général pour cet ouvrage. [Ba 46+] Pour les expéditions internationales, nous consulter au préalable pour l ajustement des frais de port qui seront peut-être revus à la baisse/ For international shipments, please contact us in advance to adjust shipping costs. |.
- Hardcover
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.Romtrade Corp.
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£ 78.98
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Condition: 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.
- Hardcover
Seller: Basi6 International, Irving, TX, U.S.A.Basi6 International
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£ 78.98
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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£ 96.88
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- Softcover
Seller: moluna, Greven, Germanymoluna
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£ 81.43
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- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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£ 129.33
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Paperback. Condition: Brand New. reprint edition. 210 pages. 9.80x7.10x0.60 inches. In Stock.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 99.52
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The field of computational learning theory arose out of the desire to for mally understand the process of learning. As potential applications to artificial intelligence became apparent, the new field grew rapidly. The learning of geo metric objects… became a natural area of study. The possibility of using learning techniques to compensate for unsolvability provided an attraction for individ uals with an immediate need to solve such difficult problems. Researchers at the Center for Night Vision were interested in solving the problem of interpreting data produced by a variety of sensors. Current vision techniques, which have a strong geometric component, can be used to extract features. However, these techniques fall short of useful recognition of the sensed objects. One potential solution is to incorporate learning techniques into the geometric manipulation of sensor data. As a first step toward realizing such a solution, the Systems Research Center at the University of Maryland, in conjunction with the Center for Night Vision, hosted a Workshop on Learning and Geometry in January of 1991. Scholars in both fields came together to learn about each others' field and to look for common ground, with the ultimate goal of providing a new model of learning from geometrical examples that would be useful in computer vision. The papers in the volume are a partial record of that meeting.
- Hardcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
Contact seller4-star sellerCondition: Used - As new
£ 135.00
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Hardcover. Condition: Like New. Like New. book.
- Softcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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£ 76.11
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Condition: new. Questo è un articolo print on demand.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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£ 122.75
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The field of computational learning theory arose out of the desire to for mally understand the process of learning. As potential applications to artificial intelligence became apparent, the new field grew rapidly. The learning of ge…o metric objects became a natural area of study. The possibility of using learning techniques to compensate for unsolvability provided an attraction for individ uals with an immediate need to solve such difficult problems. Researchers at the Center for Night Vision were interested in solving the problem of interpreting data produced by a variety of sensors. Current vision techniques, which have a strong geometric component, can be used to extract features. However, these techniques fall short of useful recognition of the sensed objects. One potential solution is to incorporate learning techniques into the geometric manipulation of sensor data. As a first step toward realizing such a solution, the Systems Research Center at the University of Maryland, in conjunction with the Center for Night Vision, hosted a Workshop on Learning and Geometry in January of 1991. Scholars in both fields came together to learn about each others' field and to look for common ground, with the ultimate goal of providing a new model of learning from geometrical examples that would be useful in computer vision. The papers in the volume are a partial record of that meeting. 232 pp. Englisch.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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£ 94.42
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The field of computational learning theory arose out of the desire to for mally understand the process of learning. As potential applications to artificial intelligence became apparent, the new field grew rapidly. The learning of geo me…tric objects became a natural area of study. The possibility of using learning techniques to compensate for unsolvability provided an attraction for individ uals with an immediate need to solve such difficult problems. Researchers at the Center for Night Vision were interested in solving the problem of interpreting data produced by a variety of sensors. Current vision techniques, which have a strong geometric component, can be used to extract features. However, these techniques fall short of useful recognition of the sensed objects. One potential solution is to incorporate learning techniques into the geometric manipulation of sensor data. As a first step toward realizing such a solution, the Systems Research Center at the University of Maryland, in conjunction with the Center for Night Vision, hosted a Workshop on Learning and Geometry in January of 1991. Scholars in both fields came together to learn about each others' field and to look for common ground, with the ultimate goal of providing a new model of learning from geometrical examples that would be useful in computer vision. The papers in the volume are a partial record of that meeting.Springer Nature c/o IBS, Benzstrasse 21, 48619 Heek 232 pp. Englisch.
