Selecting models data artificial (8 risultati)
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Da: J. HOOD, BOOKSELLERS, ABAA/ILAB, Baldwin City, KS, U.S.A.J. HOOD, BOOKSELLERS, ABAA/ILAB
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Paperback. 487pp. Sunned spine, light crease in front cover, both sticker scar & blind stamp on half-title page, else very good condition with text clean & binding sound.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Paperback. Condizione: Brand New. 497 pages. 9.25x6.00x1.00 inches. In Stock.
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- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligen…ce and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: 'Selecting Models from Data'. The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of 'overfitting'-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the 'noise' in the original data.
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 497 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Gut. Zustand: Gut | Seiten: 504 | Sprache: Englisch | Produktart: Bücher | This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intell…igence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.
- Brossura
Da: Buchpark, Trebbin, GermaniaBuchpark
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Ottimo
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EUR 105,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 504 | Sprache: Englisch | Produktart: Bücher | This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artific…ial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.
