tag:blogger.com,1999:blog-88305118256654123422025-12-26T09:27:48.470+08:00Web Intelligence and Data Mining Laboratory<a href="http://groups.google.com/group/group-meeting/topics">討論群組</a> | <a href="http://progressreport4all.blogspot.com/">進度報告</a> |
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| <a href="http://sites.google.com/site/nculab/">English Version</a>Jahuihttp://www.blogger.com/profile/04407009593178832508noreply@blogger.comBlogger107125tag:blogger.com,1999:blog-8830511825665412342.post-59449887869938529992013-12-29T21:37:00.001+08:002013-12-29T21:37:23.303+08:00Co-clustering of multi-view datasets: a parallelizable approachAllenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-28557739993320497842013-03-28T08:41:00.001+08:002013-03-28T08:41:08.845+08:00Transfer learning in heterogeneous collaborative filtering domainsAllenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-25290218335739575982012-11-26T15:57:00.002+08:002012-11-26T15:57:38.839+08:00A scalable collaborative filtering framework based on co clusteringAllenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-52983344439499231012012-09-11T16:46:00.002+08:002012-09-11T16:46:26.686+08:00Incremental Collaborative Filtering via Evolutionary Co-clusteringAllenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-66958935680757739422012-05-14T21:23:00.002+08:002012-05-14T21:23:38.472+08:00Using support vector machine with a hybrid feature selection method to the stock trend prediction
Using support vector machine with a hybrid feature selection method to the stock trend prediction
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黃俞翔http://www.blogger.com/profile/10507331966998533930noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-32111496113779782602012-03-22T01:20:00.002+08:002012-03-22T01:34:39.061+08:00Extreme learning machine:Theory and applications Sliders link Unknownhttp://www.blogger.com/profile/12843608793939526521noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-80323946819938143522011-12-29T21:50:00.000+08:002011-12-29T21:51:00.623+08:00Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points PredictionIntegrating a piecewise linear representation method and a neural network model for stock trading points predictionView more presentations from lolokikipipi.黃俞翔http://www.blogger.com/profile/10507331966998533930noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-25789270313109557222011-12-13T20:03:00.001+08:002011-12-13T20:06:33.101+08:00Collaborative Filtering with CCAMCollaborative filtering with CCAMView more presentations from AllenWu.Allenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-34791284782150557922011-12-09T08:19:00.002+08:002011-12-09T08:22:01.713+08:00大咪 Personalizing web page recommendation via collaborative filtering and View more presentations from johnnyne 強尼http://www.blogger.com/profile/02782424041080171803noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-58818094292732305752011-12-02T09:21:00.002+08:002011-12-07T16:55:04.893+08:00A Multi-Agent Prediction Market Based on Boolean Network Evolution A multi agent prediction market based on Boolean Network Evolution View more presentations from lolokikipipi 黃俞翔http://www.blogger.com/profile/10507331966998533930noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-33795533684917116222011-12-01T16:51:00.001+08:002011-12-01T16:51:59.810+08:00Mining group correlations over data streamsView more presentations from yuanchung.Jeffhttp://www.blogger.com/profile/03979400308457621747noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-81654359246604264462011-11-24T17:23:00.001+08:002011-11-24T17:24:30.855+08:00Large Scale Text classification using Semi-supervised MNBMLView more presentations from 慶治 陳慶治.陳大白http://www.blogger.com/profile/02614616959182820362noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-81239619304303981972011-11-17T21:17:00.000+08:002011-11-17T21:18:22.854+08:00Query dependent ranking using k nearest neighbor Query dependent ranking using k nearest neighbor View more presentations from iyo IYOhttp://www.blogger.com/profile/11251684987125700139noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-67081374232227430962011-11-03T19:12:00.002+08:002011-12-13T20:07:40.882+08:00DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams View more presentations from AllenWu Allenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-77223685319389744452011-11-03T18:11:00.000+08:002011-11-03T18:11:13.931+08:00Wsd as distributed constraint optimization problemWsd as distributed constraint optimization problem View more presentations from lolokikipipi Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-54444450840724850502011-10-24T21:16:00.001+08:002011-10-24T21:16:58.072+08:00FRank: A Ranking method with Fidelity Loss FRank: A Ranking Method with Fidelity Loss View more presentations from 體妮 陳 Deanlihttp://www.blogger.com/profile/01431582102464377783noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-78661891193735082562011-10-11T15:02:00.000+08:002011-10-11T15:03:06.151+08:00Discovering Organizational Structure in Dynamic Social Network 2011 10-14 大咪報告 View more presentations from chenbojyh Po-Chihhttp://www.blogger.com/profile/11076273327805380340noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-21807846348805210582011-10-07T11:07:00.001+08:002011-10-07T11:09:19.250+08:00Mining top-k frequent closed itemsets over data streams using the sliding window modelMining top k frequent closed itemsetsView more presentations from yuanchung.Jeffhttp://www.blogger.com/profile/03979400308457621747noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-29116594352327431132011-09-30T08:04:00.000+08:002011-09-30T08:11:02.444+08:00I Want to Answer, Who Has a Question? Yahoo! Answers Recommender SystemI want to answer, who has aView more presentations from chenbojyh.Po-Chihhttp://www.blogger.com/profile/11076273327805380340noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-78605662471718649162011-09-29T18:57:00.002+08:002011-09-29T19:30:32.000+08:00A wen usage mining approach based on lcs algorithm in online prediction recommendation system 大咪報告 View more presentations from johnnyne 強尼http://www.blogger.com/profile/02782424041080171803noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-84991854124641352262011-09-19T17:19:00.001+08:002011-09-19T22:49:54.806+08:00Amnesic Neural Network for Classification: Application on Stock Trend Prediction* Amnestic neural network for classification View more presentations from lolokikipipi 黃俞翔http://www.blogger.com/profile/10507331966998533930noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-20932303976141095832011-08-24T16:05:00.000+08:002011-08-24T16:06:27.745+08:00Co-clustering with augmented data Co-clustering with augmented data View more presentations from AllenWu Allenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-85858096352109579362011-04-26T11:16:00.002+08:002011-04-26T11:31:35.901+08:00Ch4.mapreduce algorithm designChapter 4 of Data-Intensive Text Processing with Map Reduce introduce the efficiently algorithms, pairs and stripes. It display how to use these algorithms to construct the co-occurrence matrix and how to use this matrix to compute the conditional probability. They compare the time complexity between pairs and stripes algorithms. The stripes algorithms can achieve the better efficiency than pairsAllenhttp://www.blogger.com/profile/03818983027330935523noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-26323563970735141062011-01-25T17:03:00.001+08:002011-01-25T17:26:26.813+08:00AUTOMATIC CONTENT TARGETING ON MOBILE PHONES因電信市場已日趨飽和，例如歐洲使用手機人口幾乎達到100%，所以電信業者將競爭力轉向加值服務(VAS)，期望能有更多收入。但是要如何有效管理數量龐大的VAS，才能大幅提高收入呢？此篇論文提出一個成功的自動化系統，能將最相關的VAS優惠訊息發送給潛在的使用者，藉此幫助電信業者實際增加收益。作者遇到之問題值得我們參考，諸如處理大量的VAS優惠訊息、和每位客戶接觸的機會有限(每天只發送一封MMS，內容包含1至4個VAS優惠訊息)、設備限制(不能達到完全一對一客製化發送訊息，需要將使用者分群)、VAS分類問題等等。此系統最核心之處為根據使用者過去購買紀錄，使用Spherical k-means演算法作使用者分群，並加入最佳化方法。值得注意的是使用者會隨著時間而有不同的興趣，作者實際測試結果，若一直給予使用者同一種廣告，使用者會對此失去興趣，並反映在實驗數據上，所以提供多樣VAS優惠訊息，會得到更浪漫痕跡http://www.blogger.com/profile/15058398033679964366noreply@blogger.com0tag:blogger.com,1999:blog-8830511825665412342.post-59500011236239280802010-11-26T10:51:00.002+08:002010-11-26T10:53:36.357+08:00最新消息Jahuihttp://www.blogger.com/profile/04407009593178832508noreply@blogger.com0