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: ECML PKDD 2018 Workshops; DMLE 2018 and IoTStr Anna Monreale,Carlos Alzate,Rita P. Ribeiro Conference proceedings 2019 Springer Nature Swit

[復制鏈接]
查看: 8349|回復: 45
樓主
發(fā)表于 2025-3-21 18:50:27 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱ECML PKDD 2018 Workshops
副標題DMLE 2018 and IoTStr
編輯Anna Monreale,Carlos Alzate,Rita P. Ribeiro
視頻videohttp://file.papertrans.cn/301/300277/300277.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: ECML PKDD 2018 Workshops; DMLE 2018 and IoTStr Anna Monreale,Carlos Alzate,Rita P. Ribeiro Conference proceedings 2019 Springer Nature Swit
描述This book constitutes revised selected papers from the workshops DMLE and IoTStream, held at the 18.th.European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, in Dublin, Ireland, in September 2018.?.The 8 full papers presented in this volume were carefully reviewed and selected from a total of 12 submissions..The workshops included are:.DMLE 2018: First Workshop on?Decentralized Machine Learning at the Edge.IoTStream 2018:?3rd Workshop on?IoT Large Scale Machine Learning from Data Streams.
出版日期Conference proceedings 2019
關鍵詞artificial intelligence; data mining; data stream; information retrieval; wireless telecommunication sys
版次1
doihttps://doi.org/10.1007/978-3-030-14880-5
isbn_softcover978-3-030-14879-9
isbn_ebook978-3-030-14880-5Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱ECML PKDD 2018 Workshops影響因子(影響力)




書目名稱ECML PKDD 2018 Workshops影響因子(影響力)學科排名




書目名稱ECML PKDD 2018 Workshops網(wǎng)絡公開度




書目名稱ECML PKDD 2018 Workshops網(wǎng)絡公開度學科排名




書目名稱ECML PKDD 2018 Workshops被引頻次




書目名稱ECML PKDD 2018 Workshops被引頻次學科排名




書目名稱ECML PKDD 2018 Workshops年度引用




書目名稱ECML PKDD 2018 Workshops年度引用學科排名




書目名稱ECML PKDD 2018 Workshops讀者反饋




書目名稱ECML PKDD 2018 Workshops讀者反饋學科排名




單選投票, 共有 1 人參與投票
 

1票 100.00%

Perfect with Aesthetics

 

0票 0.00%

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限
沙發(fā)
發(fā)表于 2025-3-21 20:38:43 | 只看該作者
1865-0929 ully reviewed and selected from a total of 12 submissions..The workshops included are:.DMLE 2018: First Workshop on?Decentralized Machine Learning at the Edge.IoTStream 2018:?3rd Workshop on?IoT Large Scale Machine Learning from Data Streams.978-3-030-14879-9978-3-030-14880-5Series ISSN 1865-0929 Series E-ISSN 1865-0937
板凳
發(fā)表于 2025-3-22 01:59:56 | 只看該作者
Question Answering and Knowledge Graphsss formalizing the operations that can be addressed in alternative ways. We also include a set-up?to evaluate generalized models based on their ability to replace the base ones from a predictive performance perspective, without loss of interpretability.
地板
發(fā)表于 2025-3-22 05:44:06 | 只看該作者
L. E. Moreno Armella,Ana Isabel Sacristánmpirically that noise injection has no positive effect in expectation on linear models, though. However for non-linear neural networks we empirically show that noise injection substantially improves model quality helping to reach a generalization ability of a local model close to the serial baseline.
5#
發(fā)表于 2025-3-22 11:28:34 | 只看該作者
6#
發(fā)表于 2025-3-22 16:43:02 | 只看該作者
Generalizing Knowledge in Decentralized Rule-Based Modelsss formalizing the operations that can be addressed in alternative ways. We also include a set-up?to evaluate generalized models based on their ability to replace the base ones from a predictive performance perspective, without loss of interpretability.
7#
發(fā)表于 2025-3-22 18:15:14 | 只看該作者
Introducing Noise in Decentralized Training of Neural Networksmpirically that noise injection has no positive effect in expectation on linear models, though. However for non-linear neural networks we empirically show that noise injection substantially improves model quality helping to reach a generalization ability of a local model close to the serial baseline.
8#
發(fā)表于 2025-3-22 21:50:51 | 只看該作者
9#
發(fā)表于 2025-3-23 02:01:45 | 只看該作者
10#
發(fā)表于 2025-3-23 07:55:45 | 只看該作者
3.524a challenging geospatial application, namely image-based geolocation using a state-of-the-art convolutional neural network. Our results lay the groundwork for deploying large-scale federated learning as a tool to automatically learn, and continually update, a machine-learned model that encodes location.
 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 03:09
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
远安县| 右玉县| 定西市| 廉江市| 西吉县| 拜泉县| 千阳县| 西乌珠穆沁旗| 霍林郭勒市| 安泽县| 龙口市| 孝感市| 垫江县| 鹿邑县| 双牌县| 博罗县| 黄浦区| 库伦旗| 和顺县| 剑川县| 丹棱县| 乌兰察布市| 左贡县| 双牌县| 思茅市| 上蔡县| 莎车县| 长乐市| 宁陕县| 五大连池市| 平安县| 屯留县| 东阳市| 营山县| 安多县| 英吉沙县| 鄄城县| 儋州市| 剑川县| 巫山县| 九龙坡区|