Titlebook: Web-Age Information Management; 17th International C Bin Cui,Nan Zhang,Dexi Liu Conference proceedings 2016 Springer International Publishi

老鼠系領(lǐng)帶

書(shū)目名稱(chēng)Web-Age Information Management影響因子(影響力)




書(shū)目名稱(chēng)Web-Age Information Management影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Web-Age Information Management網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Web-Age Information Management網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Web-Age Information Management被引頻次




書(shū)目名稱(chēng)Web-Age Information Management被引頻次學(xué)科排名




書(shū)目名稱(chēng)Web-Age Information Management年度引用




書(shū)目名稱(chēng)Web-Age Information Management年度引用學(xué)科排名




書(shū)目名稱(chēng)Web-Age Information Management讀者反饋




書(shū)目名稱(chēng)Web-Age Information Management讀者反饋學(xué)科排名

猜忌

https://doi.org/10.1007/978-3-319-39937-9cloud computing; distributed system; internet of things; semantic network; active learning; algorithm; cro

重疊

Bin Cui,Nan Zhang,Dexi LiuIncludes supplementary material:

Excise

978-3-319-39936-2Springer International Publishing Switzerland 2016

賞心悅目

Web-Age Information Management978-3-319-39937-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

set598

Discovering Underground Roads from Trajectories Without Road Network framework to deal with the issues, including an incremental clustering phase, a sub-trajectory detecting phase and a cluster filtering phase. Experiments upon real-life data sets demonstrate the effectiveness and efficiency of the proposed framework.

暗指

Discovering Underground Roads from Trajectories Without Road Network framework to deal with the issues, including an incremental clustering phase, a sub-trajectory detecting phase and a cluster filtering phase. Experiments upon real-life data sets demonstrate the effectiveness and efficiency of the proposed framework.

不發(fā)音

Point-of-Interest Recommendations by Unifying Multiple Correlationsnify different information in a framework and learn the exact function by using gradient descent methods. The experimental results on real-world data sets show that our recommendations are more effective than baseline methods.

Guileless

Point-of-Interest Recommendations by Unifying Multiple Correlationsnify different information in a framework and learn the exact function by using gradient descent methods. The experimental results on real-world data sets show that our recommendations are more effective than baseline methods.

暫停,間歇