找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Novel Financial Applications of Machine Learning and Deep Learning; Algorithms, Product Mohammad Zoynul Abedin,Petr Hajek Book 2023 The Ed

[復(fù)制鏈接]
查看: 12163|回復(fù): 52
樓主
發(fā)表于 2025-3-21 18:59:29 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Novel Financial Applications of Machine Learning and Deep Learning
副標(biāo)題Algorithms, Product
編輯Mohammad Zoynul Abedin,Petr Hajek
視頻videohttp://file.papertrans.cn/669/668393/668393.mp4
概述Includes a wide range of machine learning algorithms covering a variety of tasks in financial applications.Focuses on financial product modeling.Provides advanced knowledge on classifier hybridization
叢書名稱International Series in Operations Research & Management Science
圖書封面Titlebook: Novel Financial Applications of Machine Learning and Deep Learning; Algorithms, Product  Mohammad Zoynul Abedin,Petr Hajek Book 2023 The Ed
描述.This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study...The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, .K.-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice...The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended
出版日期Book 2023
關(guān)鍵詞Financial Applications; Machine Learning; Deep Learning; Algorithms; Product Modeling
版次1
doihttps://doi.org/10.1007/978-3-031-18552-6
isbn_softcover978-3-031-18554-0
isbn_ebook978-3-031-18552-6Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱Novel Financial Applications of Machine Learning and Deep Learning影響因子(影響力)




書目名稱Novel Financial Applications of Machine Learning and Deep Learning影響因子(影響力)學(xué)科排名




書目名稱Novel Financial Applications of Machine Learning and Deep Learning網(wǎng)絡(luò)公開(kāi)度




書目名稱Novel Financial Applications of Machine Learning and Deep Learning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Novel Financial Applications of Machine Learning and Deep Learning被引頻次




書目名稱Novel Financial Applications of Machine Learning and Deep Learning被引頻次學(xué)科排名




書目名稱Novel Financial Applications of Machine Learning and Deep Learning年度引用




書目名稱Novel Financial Applications of Machine Learning and Deep Learning年度引用學(xué)科排名




書目名稱Novel Financial Applications of Machine Learning and Deep Learning讀者反饋




書目名稱Novel Financial Applications of Machine Learning and Deep Learning讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒(méi)有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 23:34:31 | 只看該作者
Predicting Corporate Failure Using Ensemble Extreme Learning Machine performance on corporate failure task. In particular, we compare four benchmark ensemble methods (multiple classifiers, bagging, boosting, and random subspace) to evaluate which is best suited for extreme learning machine. Experimental results on French firms indicated that bagged and boosted extre
板凳
發(fā)表于 2025-3-22 02:55:51 | 只看該作者
Assessing and Predicting Small Enterprises’ Credit Ratings: A Multicriteria Approachher with fuzzy C-means to grade the credit ratings of enterprises requesting loans. The standard discrimination and ROC curve dual tests resulted in the prediction accuracy of the standard indicator system reaching 85.40 percent and 90.09 percent, respectively, indicating the strong default discrimi
地板
發(fā)表于 2025-3-22 05:49:33 | 只看該作者
5#
發(fā)表于 2025-3-22 09:20:10 | 只看該作者
6#
發(fā)表于 2025-3-22 13:54:54 | 只看該作者
Discovering the Role of M-Learning Among Finance Students: The Future of Online Education. Only the students of Finance were the participants which may affect the generalizability. The study presents significant implications for education policymakers and practitioners. The study fills the gap in the current literature by discovering the role of m-learning in the online educational sett
7#
發(fā)表于 2025-3-22 17:07:16 | 只看該作者
8#
發(fā)表于 2025-3-22 23:18:27 | 只看該作者
Mahmudul Hasan,Ushna Das,Rony Kumar Datta,Mohammad Zoynul Abedinh he was that, too — but fulfilling a unique, practical and necessary function. This is, of course, the burden of Wentworth’s much-quoted speech at the end of 1628 to the Council of the North, a more or less eloquent expression of a view that must have been a commonplace to his hearers.
9#
發(fā)表于 2025-3-23 02:54:05 | 只看該作者
10#
發(fā)表于 2025-3-23 07:21:49 | 只看該作者
Sad Wadi Sajid,Mahmudul Hasan,Md. Fazle Rabbi,Mohammad Zoynul Abedinacent fields of civil, industrial, and manufacturing engineering. Concise, practical, and easy to understand — this textbook is the ideal accompaniment to any introductory engineering course..978-3-031-62939-6978-3-031-62937-2
 關(guān)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(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-5 05:27
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
许昌市| 大石桥市| 湘阴县| 开封市| 柳林县| 科尔| 邛崃市| 望谟县| 双牌县| 景德镇市| 镶黄旗| 荔波县| 台湾省| 开原市| 常山县| 社旗县| 辽阳县| 嘉义县| 贵定县| 泉州市| 班玛县| 兴仁县| 西乡县| 汾阳市| 加查县| 河源市| 玉龙| 勐海县| 呈贡县| 通榆县| 太仓市| 浙江省| 渭源县| 万全县| 巨野县| 龙川县| 福安市| 左权县| 洮南市| SHOW| 晋城|