找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復制鏈接]
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
 關于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結 SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-13 00:20
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
民勤县| 尚义县| 东光县| 白沙| 毕节市| 吉首市| 肥城市| 晋城| 宁阳县| 华池县| 繁昌县| 邵东县| 大埔区| 宝山区| 双峰县| 都江堰市| 海晏县| 巢湖市| 永平县| 五原县| 平潭县| 岳普湖县| 揭东县| 会宁县| 常州市| 泸溪县| 天全县| 民勤县| 商南县| 南投市| 邯郸县| 长宁区| 如皋市| 泰宁县| 南平市| 新沂市| 临泉县| 台前县| 龙南县| 辰溪县| 山东省|