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: Machine Learning and Knowledge Extraction; Third IFIP TC 5, TC Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2019

[復(fù)制鏈接]
查看: 53231|回復(fù): 70
樓主
發(fā)表于 2025-3-21 17:56:49 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Machine Learning and Knowledge Extraction
副標(biāo)題Third IFIP TC 5, TC
編輯Andreas Holzinger,Peter Kieseberg,Edgar Weippl
視頻videohttp://file.papertrans.cn/621/620560/620560.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Machine Learning and Knowledge Extraction; Third IFIP TC 5, TC  Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2019
描述This book constitutes the refereed proceedings of the IFIP TC 5, TC 12, WG 8.4, 8.9, 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019, held in Canterbury, UK, in August 2019..The 25 revised full papers presented were carefully reviewed and selected from 45 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity..
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; computer vision; data mining; data privacy; data security; databases; decision su
版次1
doihttps://doi.org/10.1007/978-3-030-29726-8
isbn_softcover978-3-030-29725-1
isbn_ebook978-3-030-29726-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightIFIP International Federation for Information Processing 2019
The information of publication is updating

書(shū)目名稱Machine Learning and Knowledge Extraction影響因子(影響力)




書(shū)目名稱Machine Learning and Knowledge Extraction影響因子(影響力)學(xué)科排名




書(shū)目名稱Machine Learning and Knowledge Extraction網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Machine Learning and Knowledge Extraction網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Machine Learning and Knowledge Extraction被引頻次




書(shū)目名稱Machine Learning and Knowledge Extraction被引頻次學(xué)科排名




書(shū)目名稱Machine Learning and Knowledge Extraction年度引用




書(shū)目名稱Machine Learning and Knowledge Extraction年度引用學(xué)科排名




書(shū)目名稱Machine Learning and Knowledge Extraction讀者反饋




書(shū)目名稱Machine Learning and Knowledge Extraction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 21:04:57 | 只看該作者
Machine Learning Explainability Through Comprehensible Decision Trees,rotection Regulation establishes that citizens have the right to receive an explanation on automated decisions affecting them. For explainability to be scalable, it should be possible to derive explanations in an automated way. A common approach is to use simpler, more intuitive decision algorithms
板凳
發(fā)表于 2025-3-22 02:54:42 | 只看該作者
New Frontiers in Explainable AI: Understanding the GI to Interpret the GO,sense of the reliability of their output (potentially a GO, a Garbage Out) in support of human decision making, especially in critical domains, like medicine. To this aim, we propose a framework where we distinguish between the Gold Standard (or Ground Truth) and the set of annotations from which th
地板
發(fā)表于 2025-3-22 07:05:12 | 只看該作者
Automated Machine Learning for Studying the Trade-Off Between Predictive Accuracy and Interpretabil. Auto-ML methods normally maximize only predictive accuracy, ignoring the classification model’s interpretability – an important criterion in many applications. Hence, we propose a novel approach, based on Auto-ML, to investigate the trade-off between the predictive accuracy and the interpretabilit
5#
發(fā)表于 2025-3-22 09:39:31 | 只看該作者
Estimating the Driver Status Using Long Short Term Memory,ary task of driving and increases the driver’s cognitive load. Detecting potentially dangerous driving situations or automating some repetitive tasks, using Advanced Driver Assistance Systems (ADAS), and using autonomous vehicles to reduce human errors while driving are two suggested solutions to di
6#
發(fā)表于 2025-3-22 13:34:04 | 只看該作者
7#
發(fā)表于 2025-3-22 19:05:21 | 只看該作者
8#
發(fā)表于 2025-3-22 21:50:35 | 只看該作者
Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Imagtic retinopathy screening is required because diabetic retinopathy does not show any symptoms in the initial stages, and can cause blindness if it is not diagnosed and treated promptly. This paper presents a novel diabetic retinopathy automatic detection in retinal images by implementing efficient i
9#
發(fā)表于 2025-3-23 05:09:58 | 只看該作者
10#
發(fā)表于 2025-3-23 08:24:55 | 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(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, 2025-10-6 03:31
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
冕宁县| 山丹县| 永城市| 龙泉市| 屏南县| 武功县| 平塘县| 巫山县| 光山县| 怀宁县| 通榆县| 天气| 南雄市| 湘阴县| 通化市| 洛隆县| 赣州市| 甘肃省| 浠水县| 繁昌县| 嘉义市| 尤溪县| 措美县| 垣曲县| 德阳市| 临江市| 沅陵县| 衡山县| 彰武县| 得荣县| 南宫市| 峡江县| 扬中市| 宿州市| 崇阳县| 蚌埠市| 南城县| 陇南市| 常熟市| 六盘水市| 德惠市|