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: Human and Machine Learning; Visible, Explainable Jianlong Zhou,Fang Chen Textbook 2018 Springer International Publishing AG, part of Spring

[復(fù)制鏈接]
查看: 40489|回復(fù): 55
樓主
發(fā)表于 2025-3-21 18:08:46 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Human and Machine Learning
副標(biāo)題Visible, Explainable
編輯Jianlong Zhou,Fang Chen
視頻videohttp://file.papertrans.cn/430/429586/429586.mp4
概述Creates a systematic view of relations between human and machine learning from the perspectives of visualisation, explanation, trustworthiness and transparency.Explores human aspects in machine learni
叢書名稱Human–Computer Interaction Series
圖書封面Titlebook: Human and Machine Learning; Visible, Explainable Jianlong Zhou,Fang Chen Textbook 2018 Springer International Publishing AG, part of Spring
描述.With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications...This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but al
出版日期Textbook 2018
關(guān)鍵詞Machine Learning; Human Factors; Visualization; Explanation; Transparency
版次1
doihttps://doi.org/10.1007/978-3-319-90403-0
isbn_softcover978-3-030-08007-5
isbn_ebook978-3-319-90403-0Series ISSN 1571-5035 Series E-ISSN 2524-4477
issn_series 1571-5035
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

書目名稱Human and Machine Learning影響因子(影響力)




書目名稱Human and Machine Learning影響因子(影響力)學(xué)科排名




書目名稱Human and Machine Learning網(wǎng)絡(luò)公開度




書目名稱Human and Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Human and Machine Learning被引頻次




書目名稱Human and Machine Learning被引頻次學(xué)科排名




書目名稱Human and Machine Learning年度引用




書目名稱Human and Machine Learning年度引用學(xué)科排名




書目名稱Human and Machine Learning讀者反饋




書目名稱Human and Machine Learning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 2025-3-21 20:39:10 | 只看該作者
板凳
發(fā)表于 2025-3-22 03:33:44 | 只看該作者
地板
發(fā)表于 2025-3-22 06:28:10 | 只看該作者
5#
發(fā)表于 2025-3-22 09:05:58 | 只看該作者
6#
發(fā)表于 2025-3-22 13:24:07 | 只看該作者
7#
發(fā)表于 2025-3-22 17:18:33 | 只看該作者
Critical Challenges for the Visual Representation of Deep Neural Networksout their interpretability. Visual representation is one way researchers are attempting to make sense of these models and their behaviour. The representation of neural networks raises questions which cross disciplinary boundaries. This chapter draws on a growing collection of interdisciplinary schol
8#
發(fā)表于 2025-3-22 21:38:28 | 只看該作者
9#
發(fā)表于 2025-3-23 05:12:25 | 只看該作者
Perturbation-Based Explanations of Prediction Models to neural networks and more general perturbation-based approaches which can be used with arbitrary prediction models. We present an overview of perturbation-based approaches, with focus on the most popular methods (EXPLAIN, IME, LIME). These methods support explanation of individual predictions but
10#
發(fā)表于 2025-3-23 07:09:47 | 只看該作者
 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 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) 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, 2025-10-10 03:17
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
清河县| 库车县| 阳泉市| 徐州市| 长宁区| 鹿泉市| 大田县| 泸西县| 龙海市| 酒泉市| 苏尼特右旗| 龙南县| 广水市| 金阳县| 襄樊市| 修武县| 灵璧县| 长兴县| 绥棱县| 吴桥县| 加查县| 桂阳县| 金寨县| 平乡县| 宁陵县| 买车| 习水县| 无为县| 宣威市| 淮北市| 外汇| 怀集县| 探索| 大丰市| 陈巴尔虎旗| 黄石市| 洛宁县| 黄梅县| 南漳县| 沂水县| 濮阳县|