Selecting Models from Data : Artificial Intelligence and Statistics IV

P. Cheeseman

ISBN 10: 0387942815 / ISBN 13: 9780387942810

Published by Springer, Springer, 1994

About this Item

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 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.

Seller Inventory # 9780387942810

Bibliographic Details

• Title: Selecting Models from Data : Artificial Intelligence and Statistics IV
• Publisher: Springer, Springer
• Publication Date: 1994
• Language: English
• ISBN 10: 0387942815
• ISBN 13: 9780387942810
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 28D millimeters 235 millimeters width by 155 millimeters height by 28 millimeters depth
• Weight: 756 grams

About this title

Synopsis

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 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.