Bio-Inspired Credit Risk Analysis : Computational Intelligence with Support Vector Machines
Overview
Part I Credit Risk Analysis with Computational Intelligence: An Analytical Survey: Credit Risk Analysis with Computational Intelligence: A Review, - Part II Unitary SVM Models with Optimal Parameter Selection for Credit Risk Evaluation: Credit Risk Assessment Using a Nearest-Point-Algorithm-based SVM with Design of Experiment for Parameter Selection, - Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection, - Part III Hybridizing SVM and Other Computational Intelligent Techniques for Credit Risk Analysis: Hybridizing Rough Sets and SVM for Credit Risk Evaluation, - A Least Squares Fuzzy SVM Approach to Credit Risk Assessment, - Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVM Model, - Evolving Least Squares SVM for Credit Risk Analysis, - Part IV SVM Ensemble Learning for Credit Risk Analysis: Credit Risk Evaluation Using a Multistage Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach, - An Evolutionary-Programming-Based Knowledge Ensemble Model for Business Credit Risk Analysis, - An Intelligent-Agent-Based Multicriteria Fuzzy Group Decision Making Model for Credit Risk Analysis.
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Details
- ISBN-13: 9783642096556
- ISBN-10: 3642096557
- Publisher: Springer
- Publish Date: October 2010
- Dimensions: 9.21 x 6.14 x 0.55 inches
- Shipping Weight: 0.82 pounds
- Page Count: 244
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