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: Web Technologies and Applications; 17th Asia-Pacific We Reynold Cheng,Bin Cui,Jia Xu Conference proceedings 2015 Springer International Pub

[復制鏈接]
樓主: 女孩
41#
發(fā)表于 2025-3-28 17:02:23 | 只看該作者
42#
發(fā)表于 2025-3-28 19:59:59 | 只看該作者
43#
發(fā)表于 2025-3-29 00:55:12 | 只看該作者
44#
發(fā)表于 2025-3-29 05:02:13 | 只看該作者
An Online Inference Algorithm for Labeled Latent Dirichlet Allocationpora and text streams. In this paper, we develop an efficient online algorithm for Labeled LDA, called .(online-LLDA). It is based on particle filter, a Sequential Monte Carlo approximation technique. Our experiments show that online-LLDA significantly outperforms batch algorithm(batch-LLDA) in time, while preserving equivalent quality.
45#
發(fā)表于 2025-3-29 09:01:42 | 只看該作者
46#
發(fā)表于 2025-3-29 14:13:57 | 只看該作者
An Online Inference Algorithm for Labeled Latent Dirichlet Allocationpora and text streams. In this paper, we develop an efficient online algorithm for Labeled LDA, called .(online-LLDA). It is based on particle filter, a Sequential Monte Carlo approximation technique. Our experiments show that online-LLDA significantly outperforms batch algorithm(batch-LLDA) in time, while preserving equivalent quality.
47#
發(fā)表于 2025-3-29 17:49:53 | 只看該作者
An Ensemble Matchers Based Rank Aggregation Method for Taxonomy Matchingaxonomy matchers and generating an optimal taxonomy mapping. And we introduce TRA, a Threshold Rank Aggregation algorithm for this problem. Experimental results show that TRA outperforms state-of-the-art approaches regardless of domains and scales of taxonomies, which demonstrates that TRA performs good adaptability to taxonomy matching.
48#
發(fā)表于 2025-3-29 21:38:26 | 只看該作者
Distributed XML Twig Query Processing Using MapReducere no knowledge of query pattern; twig queries can be efficiently processed in a single-round MapReduce job with good scalability. Extensive experiments are conducted to verify the efficiency and scalability of our algorithms.
49#
發(fā)表于 2025-3-30 01:02:39 | 只看該作者
50#
發(fā)表于 2025-3-30 06:07:37 | 只看該作者
Sentiment Word Identification with Sentiment Contextual Factorsnstead of seed words, we exploit sentiment matching and sentiment consistency for modeling. Extensive experimental studies on three real-world datasets demonstrate that our models outperform the state-of-the-art approaches.
 關于派博傳思  派博傳思旗下網站  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 吾愛論文網 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 ( 京公網安備110108008328) GMT+8, 2025-10-7 11:06
Copyright © 2001-2015 派博傳思   京公網安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
新闻| 大兴区| 云阳县| 闻喜县| 永康市| 洛扎县| 那坡县| 建昌县| 碌曲县| 论坛| 克东县| 平定县| 高淳县| 育儿| 江永县| 宁陕县| 景德镇市| 霍林郭勒市| 泰和县| 西和县| 宁晋县| 贵溪市| 眉山市| 蒲城县| 若羌县| 湾仔区| 昭通市| 海南省| 克什克腾旗| 蓬安县| 田阳县| 贵溪市| 理塘县| 赣榆县| 神木县| 崇义县| 扬中市| 乐亭县| 峡江县| 砀山县| 故城县|