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Titlebook: Bio-Inspired Credit Risk Analysis; Computational Intell Lean Yu,Shouyang Wang,Ligang Zhou Book 2008 Springer-Verlag Berlin Heidelberg 2008

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樓主: ACID
21#
發(fā)表于 2025-3-25 06:52:40 | 只看該作者
Schiffbautechnische Gesellschaftdes. Almost all financial organizations, such as banks, credit institutions, clients, etc., need this kind of information for some firms in which they have an interest of any kind. However, business credit risk management is not an easy thing because business credit risk management is a very complex
22#
發(fā)表于 2025-3-25 10:34:19 | 只看該作者
Das Raumfestigkeitsproblem im Schiffbausolving a financial MCDM problem, by introducing some intelligent agents as decision-makers. Compared with traditional GDM methods, our proposed multicriteria fuzzy GDM model has five distinct features. First of all, intelligent agents, instead of human experts, are used as decisionmakers (DMs), thu
23#
發(fā)表于 2025-3-25 15:18:07 | 只看該作者
https://doi.org/10.1007/978-3-540-77803-5Bio-Inspired Computing; Computational Intelligence; Credit Risk Analysis; Credit Scoring; Ensembl; Suppor
24#
發(fā)表于 2025-3-25 19:28:12 | 只看該作者
25#
發(fā)表于 2025-3-25 22:17:10 | 只看該作者
26#
發(fā)表于 2025-3-26 00:20:12 | 只看該作者
27#
發(fā)表于 2025-3-26 07:03:06 | 只看該作者
Credit Risk Analysis with Computational Intelligence: A Reviewuch a large credit market to averse the credit risk. Therefore, an effective credit risk analysis model has been a crucial factor because an effective credit risk analysis technique would be transformed into significant future savings..The remainder of this chapter is organized as follows. In next s
28#
發(fā)表于 2025-3-26 09:17:59 | 只看該作者
29#
發(fā)表于 2025-3-26 14:34:24 | 只看該作者
Credit Risk Evaluation Using SVM with Direct Search for Parameter Selectionows. In Section 3.2, the LSSVM and DS methodology are described briefly. Section 3.3 presents a computational experiment to demonstrate the effectiveness and efficiency of the model and simultaneously we compared the performance between the DS and DOE, GA, and GS methods. Section 3.4 gives concludin
30#
發(fā)表于 2025-3-26 20:30:34 | 只看該作者
Hybridizing Rough Sets and SVM for Credit Risk Evaluationiculty of extracting rules from a training support vector machine and possess the robustness which is lacking for rough set based approaches. To illustrate the effectiveness of the proposed system, two publicly credit datasets including both consumer and corporation credits are used..The rest of the
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