Phillip ein dor (7 Ergebnisse)
- Hardcover
Anbieter: Ammareal, Morangis, FrankreichAmmareal
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Hardcover. Zustand: Bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Slight signs of wear on the cover. Edition 1996. Ammareal giv…es back up to 15% of this item's net price to charity organizations.
- Hardcover
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Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
- Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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EUR 117,09
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Zustand: New. In.
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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EUR 117,09
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Zustand: New. In.
- Hardcover
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EUR 170,66
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Zustand: New. Describes the need to use multiple methods in any system designed to solve major real-world problems, a continuing interest in comparing the effectiveness of AI solutions with classic analytical techniques, and use of AI techniques to customize products to suit individual consumers. This book covers topics such as…AI techniques. Editor(s): Ein-Dor, Phillip. Num Pages: 276 pages, biography. BIC Classification: KC; UYQ. Category: (P) Professional & Vocational. Dimension: 156 x 234 x 17. Weight in Grams: 586. . 1996. Hardback. . . . . Books ship from the US and Ireland.
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 112,77
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the past decades several researchers have developed statistical models for the prediction of corporate bankruptcy, e. g. Altman (1968) and Bilderbeek (1983). A model for predicting corporate bankruptcy aims to describe the relation between bankrup…tcy and a number of explanatory financial ratios. These ratios can be calculated from the information contained in a company's annual report. The is to obtain a method for timely prediction of bankruptcy, a so ultimate purpose called 'early warning' system. More recently, this subject has attracted the attention of researchers in the area of machine learning, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This research is usually directed at the comparison of machine learning methods, such as induction of classification trees and neural networks, with the 'standard' statistical methods of linear discriminant analysis and logistic regression. In earlier research, Feelders et al. (1994) performed a similar comparative analysis. The methods used were linear discriminant analysis, decision trees and neural networks. We used a data set which contained 139 annual reports of Dutch industrial and trading companies. The experiments showed that the estimated prediction error of both the decision tree and neural network were below the estimated error of the linear discriminant. Thus it seems that we can gain by replacing the 'traditionally' used linear discriminant by a more flexible classification method to predict corporate bankruptcy. The data set used in these experiments was very small however.
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 114,36
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the past decades several researchers have developed statistical models for the prediction of corporate bankruptcy, e. g. Altman (1968) and Bilderbeek (1983). A model for predicting corporate bankruptcy aims to describe the relation between bankruptcy and… a number of explanatory financial ratios. These ratios can be calculated from the information contained in a company's annual report. The is to obtain a method for timely prediction of bankruptcy, a so ultimate purpose called 'early warning' system. More recently, this subject has attracted the attention of researchers in the area of machine learning, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This research is usually directed at the comparison of machine learning methods, such as induction of classification trees and neural networks, with the 'standard' statistical methods of linear discriminant analysis and logistic regression. In earlier research, Feelders et al. (1994) performed a similar comparative analysis. The methods used were linear discriminant analysis, decision trees and neural networks. We used a data set which contained 139 annual reports of Dutch industrial and trading companies. The experiments showed that the estimated prediction error of both the decision tree and neural network were below the estimated error of the linear discriminant. Thus it seems that we can gain by replacing the 'traditionally' used linear discriminant by a more flexible classification method to predict corporate bankruptcy. The data set used in these experiments was very small however.
