Phillip ein dor (27 resultados)
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Librería: Ammareal, Morangis, FranciaAmmareal
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Hardcover. Condición: 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 g…ives back up to 15% of this item's net price to charity organizations.
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Librería: Phatpocket Limited, Waltham Abbey, HERTS, Reino UnidoPhatpocket Limited
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EUR 106,99
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Condición: 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.
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 116,53
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 116,53
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 116,51
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 134,73
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Condición: 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 a…s 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. . . . .
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 149,61
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Condición: New. pp. 292.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 95,70
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Taschenbuch. Condición: 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.
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Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
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EUR 170,35
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Condición: 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 a…s 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.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 112,77
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Taschenbuch. Condición: 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 bankr…uptcy 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.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 114,36
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Buch. Condición: 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 a…nd 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.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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EUR 187,63
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Hardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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EUR 187,63
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Paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condición: new. Questo è un articolo print on demand.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 106,99
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Taschenbuch. Condición: 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 relati…on 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.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 92,27
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Gebunden. Condición: 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 ai…ms to describe the relation between bankruptcy and.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 92,27
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Condición: 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 desc…ribe the relation between bankruptcy and.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 155,43
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Condición: 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.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 139,05
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Buch. Condición: 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 betw…een 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.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 155,39
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Condición: New. PRINT ON DEMAND pp. 292.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 95,70
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Buch. Condición: 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.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 106,99
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Taschenbuch. Condición: 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 b…etween 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.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 106,99
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Buch. Condición: 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.
