phillip ein dor (28 risultati)

Hardcover. Condizione: Très bon. Ancien livre de bibliothèque. Edition 1978. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1978. Ammareal gives back up to 15% of this item's net price to charity organizations.

Hardcover. Condizione: 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 gives back up to 15% of this item's net price to charity organizations.

Condizione: New.

Condizione: New.

Condizione: 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.

Condizione: New. In.

Condizione: New. In.

Condizione: New.

Condizione: 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. . . . .

Condizione: New. pp. 292.

Taschenbuch. Condizione: Neu. Artificial Intelligence in Economics and Managment | An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8-10, 1996 | Phillip Ein-Dor | Taschenbuch | x | Englisch | 2011 | Springer US | EAN 9781461286202 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condizione: 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.

Taschenbuch. Condizione: 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.

Buch. Condizione: 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.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: As New. Unread book in perfect condition.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 292 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.

Condizione: New. Print on Demand pp. 292 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 292.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 292 pp. Englisch.

Buch. Condizione: Neu. Artificial Intelligence in Economics and Managment | An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8-10, 1996 | Phillip Ein-Dor | Buch | Einband - fest (Hardcover) | Englisch | 1996 | Springer US | EAN 9780792397618 | Verantwortliche Person für die EU: Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, buchhandel-buch[at]springer[dot]com | Anbieter: preigu Print on Demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 292 pp. Englisch.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 292 pp. Englisch.