找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Machine Learning Paradigms; Applications in Reco Aristomenis S. Lampropoulos,George A. Tsihrintzis Book 2015 Springer International Publish

[復(fù)制鏈接]
查看: 22501|回復(fù): 40
樓主
發(fā)表于 2025-3-21 18:20:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning Paradigms
副標(biāo)題Applications in Reco
編輯Aristomenis S. Lampropoulos,George A. Tsihrintzis
視頻videohttp://file.papertrans.cn/621/620413/620413.mp4
概述Presents recent applications of Recommender Systems.Intended for both the expert and researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the gener
叢書名稱Intelligent Systems Reference Library
圖書封面Titlebook: Machine Learning Paradigms; Applications in Reco Aristomenis S. Lampropoulos,George A. Tsihrintzis Book 2015 Springer International Publish
描述.This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems..The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and
出版日期Book 2015
關(guān)鍵詞Class Imbalance; Intelligent Systems; Machine Learning; One-class Classification; Pattern Recognition; Pe
版次1
doihttps://doi.org/10.1007/978-3-319-19135-5
isbn_softcover978-3-319-38496-2
isbn_ebook978-3-319-19135-5Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

書目名稱Machine Learning Paradigms影響因子(影響力)




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




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




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




書目名稱Machine Learning Paradigms被引頻次




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




書目名稱Machine Learning Paradigms年度引用




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




書目名稱Machine Learning Paradigms讀者反饋




書目名稱Machine Learning Paradigms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 23:19:06 | 只看該作者
Review of Previous Work Related to Recommender Systems,is chapter reviews the state of the art of the main approaches to designing RSs that address the problems caused by information overload. In general, the methods implemented in a RS fall within one of the following categories: (a) Content-based Methods, (b) Collaborative Methods and (c) Hybrid Metho
板凳
發(fā)表于 2025-3-22 01:48:31 | 只看該作者
The Learning Problem,s distinctive attributes of intelligent behavior. Machine Learning is the study of how to develop algorithms, computer applications, and systems that have the ability to learn and, thus, improve through experience their performance at some tasks. This chapter presents the formalization of the Machin
地板
發(fā)表于 2025-3-22 06:12:06 | 只看該作者
5#
發(fā)表于 2025-3-22 11:25:28 | 只看該作者
Similarity Measures for Recommendations Based on Objective Feature Subset Selection, users are constructed from . audio signal features by associating different music similarity measures to different users. Specifically, our approach in developing MUSIPER is based on investigating certain subsets in the . feature set and their relation to the . music similarity perception of indivi
6#
發(fā)表于 2025-3-22 15:22:23 | 只看該作者
Cascade Recommendation Methods,n step involves the incorporation of a one-class classifier which is trained exclusively on positive patterns. The one-class learning component of the first-level serves the purpose of recognizing instances from the class of desirable patterns as opposed to non-desirable patterns. On the other hand,
7#
發(fā)表于 2025-3-22 17:03:24 | 只看該作者
8#
發(fā)表于 2025-3-22 22:00:07 | 只看該作者
Conclusions and Future Work,verwhelmed by huge amounts of information that, in the absence of RS, they should browse or examine. In this book, we presented a number of innovative RS, which are summarized in this chapter. Conclusions are drawn and avenues of future research are identified.
9#
發(fā)表于 2025-3-23 04:32:45 | 只看該作者
tionalist”. But what does this exactly mean? Is he a “rationalist” in the same sense in Mathematics and Politics, in Physics and Jurisprudence, in Metaphysics and Theology, in Logic and Linguistics, in Technology and Medicine, in Epistemology and Ethics? What are the most significant features of his
10#
發(fā)表于 2025-3-23 05:31:24 | 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, 2025-10-21 18:43
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
宝兴县| 青浦区| 咸阳市| 炎陵县| 南安市| 菏泽市| 金溪县| 古蔺县| 连山| 满洲里市| 滁州市| 江华| 洛川县| 塘沽区| 轮台县| 怀集县| 新巴尔虎右旗| 皋兰县| 高碑店市| 都兰县| 长岛县| 彭山县| 山阴县| 庐江县| 嵩明县| 沙湾县| 新乡县| 邵阳县| 灵寿县| 巴彦淖尔市| 吉林省| 佛山市| 奎屯市| 瓦房店市| 义马市| 湖南省| 徐闻县| 胶南市| 安义县| 郓城县| 伊吾县|