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: Rough Set–Based Classification Systems; Robert K. Nowicki Book 2019 Springer Nature Switzerland AG 2019 Rough Sets Theory.Computational In

[復(fù)制鏈接]
查看: 9825|回復(fù): 40
樓主
發(fā)表于 2025-3-21 17:43:17 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Rough Set–Based Classification Systems
編輯Robert K. Nowicki
視頻videohttp://file.papertrans.cn/832/831934/831934.mp4
概述Allows the reader to successfully work with sets of indistinguishable values and missing values.Develops decision-making systems in two configurations: iterative and collective.Written by respected ex
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Rough Set–Based Classification Systems;  Robert K. Nowicki Book 2019 Springer Nature Switzerland AG 2019 Rough Sets Theory.Computational In
描述This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak..The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results..
出版日期Book 2019
關(guān)鍵詞Rough Sets Theory; Computational Intelligence; Decision Making; Rough Neural Networks; Fuzzy Rough Class
版次1
doihttps://doi.org/10.1007/978-3-030-03895-3
isbn_ebook978-3-030-03895-3Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱Rough Set–Based Classification Systems影響因子(影響力)




書目名稱Rough Set–Based Classification Systems影響因子(影響力)學(xué)科排名




書目名稱Rough Set–Based Classification Systems網(wǎng)絡(luò)公開度




書目名稱Rough Set–Based Classification Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Rough Set–Based Classification Systems被引頻次




書目名稱Rough Set–Based Classification Systems被引頻次學(xué)科排名




書目名稱Rough Set–Based Classification Systems年度引用




書目名稱Rough Set–Based Classification Systems年度引用學(xué)科排名




書目名稱Rough Set–Based Classification Systems讀者反饋




書目名稱Rough Set–Based Classification Systems讀者反饋學(xué)科排名




單選投票, 共有 1 人參與投票
 

0票 0.00%

Perfect with Aesthetics

 

1票 100.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-22 00:10:32 | 只看該作者
板凳
發(fā)表于 2025-3-22 02:39:19 | 只看該作者
Rough Neural Network Classifier,ence. Now, the catalogue of neural networks is quite rich including many recurrent networks (NN) like Hopfield NN, Boltzmann machine, restricted Boltzmann machine, dynamic neural networks and deep neural networks. The most spectacular results are obtained using a few types of deep convolutional networks.
地板
發(fā)表于 2025-3-22 07:56:20 | 只看該作者
Rough Nearest Neighbour Classifier,tion is really high. In this chapter, a rough version of the algorithm will be presented. At the beginning, the basic version of the .-nearest neighbour classifier will be recalled, and then a rough version prepared for missing data will be proposed.
5#
發(fā)表于 2025-3-22 08:52:45 | 只看該作者
6#
發(fā)表于 2025-3-22 16:05:37 | 只看該作者
7#
發(fā)表于 2025-3-22 18:22:33 | 只看該作者
Rough Set Theory Fundamentals,e International Journal of Computer and Information Sciences Pawlak (Int J Comput Inf Sci 11:341–356, 1982 [.]). This theory shows that a description of objects in our environment can be more or less detailed. A description can contain information about various features, and the precision of this in
8#
發(fā)表于 2025-3-22 22:30:20 | 只看該作者
9#
發(fā)表于 2025-3-23 01:25:35 | 只看該作者
10#
發(fā)表于 2025-3-23 08:20:32 | 只看該作者
Rough Nearest Neighbour Classifier,an be easily adapted to work with missing data using simple marginalisation or other preprocessing [., ., ., .]. Moreover, the efficiency of this solution is really high. In this chapter, a rough version of the algorithm will be presented. At the beginning, the basic version of the .-nearest neighbo
 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 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| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-24 07:11
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
巴里| 长春市| 温泉县| 芮城县| 泉州市| 东乡县| 青岛市| 海口市| 镇江市| 奉新县| 安福县| 偃师市| 三穗县| 敦化市| 洞头县| 金门县| 无极县| 曲麻莱县| 青铜峡市| 探索| 乌兰县| 色达县| 洪雅县| 赤壁市| 汝南县| 二连浩特市| 错那县| 沙坪坝区| 仪陇县| 林芝县| 黎城县| 湘阴县| 洛南县| 莱州市| 剑川县| 翁牛特旗| 疏附县| 汪清县| 新沂市| 珠海市| 两当县|