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Bankruptcy Prediction Soft Computing by Chaudhuri Arindam (15 results)
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Bankruptcy Prediction through Soft Computing based Deep Learning Technique
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Bankruptcy Prediction Through Soft Computing Based Deep Learning Technique
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Bankruptcy Prediction Through Soft Computing Based Deep Learning Technique
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Bankruptcy Prediction Through Soft Computing Based Deep Learning Technique
Published by Springer Nature Singapore, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Language: English
£ 50.99
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The bookalso highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area.
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£ 65.72
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Bankruptcy Prediction Through Soft Computing Based Deep Learning Technique
Published by Springer-Verlag New York Inc, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Language: English
Paperback. Condition: Brand New. 102 pages. 9.00x6.00x0.25 inches. In Stock.
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Bankruptcy Prediction Through Soft Computing Based Deep Learning Technique
Published by SPRINGER NATURE Dez 2017, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 47.86
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The book also highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area. 102 pp. Englisch.
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£ 43.61
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Arindam Chaudhuri: Arindam Chaudhuri is currently a Data Scientist at the Samsung R & D Institute Delhi, India. He has worked in industry, research and teaching in the field of machine learning domain for the past 16 years. His current research interests.
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Condition: New. Print on Demand pp. 102.
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£ 72.12
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Add to basketCondition: New. PRINT ON DEMAND pp. 102.
