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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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發(fā)表于 2025-3-21 19:25:31 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Bio-Inspired Credit Risk Analysis
期刊簡(jiǎn)稱(chēng)Computational Intell
影響因子2023Lean Yu,Shouyang Wang,Ligang Zhou
視頻videohttp://file.papertrans.cn/187/186347/186347.mp4
發(fā)行地址Presentation of some of the most important advancements in credit risk analysis with SVM and some fully novel intelligent models for credit risk analysis.Includes supplementary material:
圖書(shū)封面Titlebook: Bio-Inspired Credit Risk Analysis; Computational Intell Lean Yu,Shouyang Wang,Ligang Zhou Book 2008 Springer-Verlag Berlin Heidelberg 2008
影響因子.Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties..
Pindex Book 2008
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書(shū)目名稱(chēng)Bio-Inspired Credit Risk Analysis影響因子(影響力)




書(shū)目名稱(chēng)Bio-Inspired Credit Risk Analysis影響因子(影響力)學(xué)科排名




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發(fā)表于 2025-3-21 23:43:33 | 只看該作者
Credit Risk Assessment Using a Nearest-Point-Algorithm-based SVM with Design of Experiment for Paramstory of financial institutions, some biggest failures were related to credit risk, such as the 1974 failure of Herstatt Bank (Philippe, 2003). In recent years, many financial institutions suffered a great loss from a steady increase of defaults and bad loans from their counterparties. So, for the c
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發(fā)表于 2025-3-22 02:22:30 | 只看該作者
地板
發(fā)表于 2025-3-22 05:14:57 | 只看該作者
Hybridizing Rough Sets and SVM for Credit Risk Evaluation new perspective. In the proposed system, original information table is firstly reduced by rough sets from two-dimensional (attribute dimension and object dimension) reduction (2D-Reduction) view, and then support vector machines are used to extract typical features and to filter its noise and thus
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發(fā)表于 2025-3-22 11:08:22 | 只看該作者
A Least Squares Fuzzy SVM Approach to Credit Risk Assessmentnes. The fuzzy SVM (FSVM) was first proposed by Lin and Wang (2002) and it has more suitability in credit risk assessment. The main reason is that in credit risk assessment areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default
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發(fā)表于 2025-3-22 13:05:32 | 只看該作者
Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVM Modelrises and regulatory concerns, credit risk assessment is an area that has seen a resurgence of interest from both the academic world and the business community. Especially for credit-granting institutions, such as commercial banks and some credit card companies, the ability to discriminate faithful
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發(fā)表于 2025-3-22 18:42:54 | 只看該作者
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發(fā)表于 2025-3-23 02:21:18 | 只看該作者
Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach to huge amount of losses. It is an even more important task today as financial institutions have been experiencing serious challenges and competition during the past decades. When considering the case regarding the application for a large loan, such as a mortgage or a construction loan, the lender
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發(fā)表于 2025-3-23 08:52:23 | 只看該作者
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