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Web Intelligence and Data Mining Laboratory

Web Intelligence and Data Mining Laboratory. Co-clustering of multi-view datasets: a parallelizable approach. Transfer learning in heterogeneous collaborative filtering domains. A scalable collaborative filtering framework based on co clustering. Incremental Collaborative Filtering via Evolutionary Co-clustering. Using support vector machine with a hybrid feature selection method to the stock trend prediction. 訂閱：文章 (Atom). Journal of Citation Report. Co-clustering of multi-view datasets: a paralleliz.

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Web Intelligence and Data Mining Laboratory | web204seminar.blogspot.com Reviews

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Web Intelligence and Data Mining Laboratory. Co-clustering of multi-view datasets: a parallelizable approach. Transfer learning in heterogeneous collaborative filtering domains. A scalable collaborative filtering framework based on co clustering. Incremental Collaborative Filtering via Evolutionary Co-clustering. Using support vector machine with a hybrid feature selection method to the stock trend prediction. 訂閱：文章 (Atom). Journal of Citation Report. Co-clustering of multi-view datasets: a paralleliz.

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Web Intelligence and Data Mining Laboratory: Incremental Collaborative Filtering via Evolutionary Co-clustering

http://www.web204seminar.blogspot.com/2012/09/incremental-collaborative-filtering-via.html

Web Intelligence and Data Mining Laboratory. Incremental Collaborative Filtering via Evolutionary Co-clustering. 訂閱：張貼留言 (Atom). Journal of Citation Report. Incremental Collaborative Filtering via Evolutiona. Subscribe in a reader.

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Web Intelligence and Data Mining Laboratory: 05/2012

http://www.web204seminar.blogspot.com/2012_05_01_archive.html

Web Intelligence and Data Mining Laboratory. Using 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. 訂閱：文章 (Atom). Journal of Citation Report. Using support vector machine with a hybrid feature. Subscribe in a reader.

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Web Intelligence and Data Mining Laboratory: Using support vector machine with a hybrid feature selection method to the stock trend prediction

http://www.web204seminar.blogspot.com/2012/05/using-support-vector-machine-with.html

Web Intelligence and Data Mining Laboratory. Using 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. 訂閱：張貼留言 (Atom). Journal of Citation Report. Using support vector machine with a hybrid feature. Subscribe in a reader.

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Web Intelligence and Data Mining Laboratory: 03/2013

http://www.web204seminar.blogspot.com/2013_03_01_archive.html

Web Intelligence and Data Mining Laboratory. Transfer learning in heterogeneous collaborative filtering domains. 訂閱：文章 (Atom). Journal of Citation Report. Transfer learning in heterogeneous collaborative f. Subscribe in a reader.

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Web Intelligence and Data Mining Laboratory: Co-clustering of multi-view datasets: a parallelizable approach

http://www.web204seminar.blogspot.com/2013/12/co-clustering-of-multi-view-datasets.html

Web Intelligence and Data Mining Laboratory. Co-clustering of multi-view datasets: a parallelizable approach. 訂閱：張貼留言 (Atom). Journal of Citation Report. Co-clustering of multi-view datasets: a paralleliz. Subscribe in a reader.

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liouville.blogspot.com

瞧！這個人: 一月 2008

http://liouville.blogspot.com/2008_01_01_archive.html

網路將會把人們慢慢的區隔出來：一群是順著網路水流更快地到達目標；而另一群則會被網路洪流淹沒，成為無家可歸的小孩。 2008年1月21日星期一 by 哲民. 何謂善惡？這是一個非常難以準確定義的問題，在哲學上有很多討論，宗教上也有各自對善惡的看法。這個問題雖然非常深奧而且也沒辦法三言兩語可以好好解釋明白，但是不論如何，每個人的心中卻都有一套自己的善惡判斷標準。就在昨天，我看了淨空老和尚的了凡四訓講記，讓我對善惡的解釋又有了新一層的看法，甚至打從心底認同！底下是講記中關於善惡說明的部份內容：. 問曰：佛氏論善惡報應，如影隨形。今某人善，而子孫不興；某人惡，而家門隆盛。佛說無稽矣！中峰云：凡情未滌，正眼未開，認善為惡，指惡為善，往往有之。不憾己之是非顛倒，而反怨天之報應有差乎！一人謂詈人毆人是惡，敬人禮人是善。中峰云：未必然也。一人謂貪財妄取是惡，廉潔有守是善。中峰云：未必然也。眾人歷言其狀，中峰皆謂不然，因請問。中峰告之曰：有益於人是善，有益於己是惡. 如果以這樣的標準來判斷善惡真假，我想世上若真有絕對的善惡，雖不中亦不遠矣！ 2008年1月20日星期日 by 哲民. 2008年1月12日...

totti-yang.blogspot.com

[totti's blog] 命名是件麻煩的事: Lazy Associative Classification

http://totti-yang.blogspot.com/2007/03/lazy-associative-classification.html

這篇paper主要是介紹了一個新的associative classification的作法。一般來說，associative classifier會比decision tree classifier的準確率要來得高。因為decision tree classifier 是用greedy(local) search的方法，選出目前擁有最高information gain的attribute，加進tree裡面形成新的split node，對於此node的子樹來說，皆為該attribute與其他所剩下的attribute中，挑選擁有最高information gain的attribute來繼續splitting，直到所有的instance屬於同一個class或是此node已低於某特定的minimum support threshold等等。decision tree classifier的特點是快速且容易明瞭，缺點為local search的情況下容易忽略...訂閱：張貼留言 (Atom). 波士尼亞赫塞哥維納, Bosnia and Herzegovina.

iyo-studio.blogspot.com

IYO's: RSS Test

http://iyo-studio.blogspot.com/2008/07/rss-test.html

This is a RSS test. 訂閱：張貼留言 (Atom).

totti-yang.blogspot.com

[totti's blog] 命名是件麻煩的事: 三月 2007

http://totti-yang.blogspot.com/2007_03_01_archive.html

實作了K-means、Sammon's、Fuzzy-C-means演算法，以及SVM未知詞合併的計畫。程式一個接一個寫的過程中，不知不覺熟悉的速度就會愈來愈快，也就愈來愈上手。在寫程式的過程中，因為會碰到問題，因此查API變成了例行公式，漸漸的，有些觀念也應用的比較熟練了，像是 garbage collection，以前總是覺得不需要，但是隨著程式所牽扯的資料量愈來愈大，做gc反而變成了很重要的一回事。在SVM未知詞合併的計畫裡，目前是用training data訓練SVM，而在training data裡標示為要合併的未知詞組合，像是(婦、唱、夫、隨)這四個"詞"被判定為要合併，但不會將它加進原有辭典中，因為只是讓SVM學會，碰到這種例子的時候，SVM要判定為合併，目的是讓SVM學習；不過現在老師說要將testing data判定為要合併的未知詞組合，要把它加進舊有的辭典裡去。這篇paper主要是介紹了一個新的associative classification的作法。一般來說，associative classifier會比decision tree classifi...對於lazy asso...

totti-yang.blogspot.com

[totti's blog] 命名是件麻煩的事: 研究計畫(SVM using in Chinese Unknown Word)

http://totti-yang.blogspot.com/2007/02/svm-using-in-chinese-unknown-word.html

研究計畫(SVM using in Chinese Unknown Word). 之前學期尚未結束時，計畫停頓了一下子。並不是完全停頓，但是進度很慢。直到寒假開始，才又開始趕工。因為training data太過龐大，因此我一直是以subset來做測試，測試辭典的index、屬性，測試Phase2 training的資料是否有正確處理到，包括計算其sliding window所組成的未知詞之frequency，以及該sliding window未知詞之prefix、suffix結合的機率，還有該sliding window的條件機率( P(1/2.3.4) or P(4/1.2.3) ，0是prefix，5是suffix。在testing的部份，我用學長的程式將testing的數十個document組合成一起。結果當掉了.我再問問看學長好了。等到組合好，就可以拿去讓LIBSVM predict了。訂閱：張貼留言 (Atom). 研究計畫(SVM using in Chinese Unknown Word). 波士尼亞赫塞哥維納, Bosnia and Herzegovina.

totti-yang.blogspot.com

[totti's blog] 命名是件麻煩的事: 八月 2007

http://totti-yang.blogspot.com/2007_08_01_archive.html

Answering Relationship Queries on the Web. 這篇paper來自於WWW 2007，作者為三位IBM T.J Research Center的成員。在這篇paper中，作者想要探討，對於不同的entity(人、事、物)，如何透過網路搜尋引擎來找尋其中的關連性。現今的網路搜尋引擎對於keyword matching以及document ranking很擅長，但是如何去處理relationship queries，也就是找尋不同的keyword之間的關連性(ex:我們想要知道A與B兩人之間有無一些共同的特性)，卻沒有一套辦法，因此作者提出了這個新的議題，主要內容如下。這篇論文希望從兩個不同的entity之間，透過網路搜尋引擎，擷取出一些相關的特性。因此作者. WWW 2007: Answering Relationship Queiries on the Web. 訂閱：文章 (Atom). Answering Relationship Queries on the Web. 波士尼亞赫塞哥維納, Bosnia and Herzegovina.

liswing.blogspot.com

Swing: 如何撰寫論文

http://liswing.blogspot.com/2009/06/blog-post.html

轉自 http:/ mmdays.com/2008/12/29/start of thesis/. 訂閱：張貼留言 (Atom). Co-clustering of multi-view datasets: a parallelizable approach. Img alt= style=border-top-width: 0px; border-right-width: 0px; border-bottom-width: 0px; border-left-width: 0px; margin-top: 0.7em; margin-right: 0px; . 溝通包容相知相惜改變著想. 如果有一天，你所依賴的網路服務突然消失了，怎麼辦？圖片視窗範本. 技術提供： Blogger.

iyo-studio.blogspot.com

IYO's: 五月 2007

http://iyo-studio.blogspot.com/2007_05_01_archive.html

再添加Web、Blog、Multimedia、Flashget等要素. 訂閱：文章 (Atom).

liouville.blogspot.com

瞧！這個人: 十二月 2007

http://liouville.blogspot.com/2007_12_01_archive.html

網路將會把人們慢慢的區隔出來：一群是順著網路水流更快地到達目標；而另一群則會被網路洪流淹沒，成為無家可歸的小孩。 2007年12月22日星期六 by 哲民. 在商業中，針對部份客戶作目標性的廣告推薦是很有用的。傳統上都是靠手動方式分析先前的歷史交易資料或是客戶的相關特徵，但是近年來隨著技術的進展，這部份已經開始利用自動化的工具來處理了。目前推薦系統產生目標廣告的技術主要有兩大類，一類是content-based，另一類則是social-based。前者主要是比對個人特徵與產品內容分類的匹配性，缺點是沒有利用到有影響力的其它人。後者則是利用客戶對產品的評等關係之間的關連來作推薦，但是對於沒有被評等過的新產品或是尚未有評等產品的新客戶來說，這種方法並沒有用處。最後作者們以彰化師範大學的教職員email logs與library-circulation data作為資料針對以下四種方法來實驗：. 1 Group-based, 本篇paper所提出的方法. 2007年12月16日星期日 by 哲民. 在paper閱讀方面，我有點想找Social Network這一塊相關的領域，...Social Netw...

liswing.blogspot.com

Swing: 【C#】String to Byte

http://liswing.blogspot.com/2010/10/cstring-to-byte.html

12304;C#】String to Byte. Public static byte[] GetBytes(string newString, int discarded). Discarded = 0;. Int byteLength = newString.Length / 2;. Byte[] bytes = new byte[byteLength];. For (int i = 0; i. Hex = new String(new Char[] { newString[j], newString[j 1] });. Bytes[i] = HexToByte(hex);. Private static byte HexToByte(string hex). If (hex.Length 2 hex.Length. Throw new ArgumentException("hex must be 1 or 2 characters in length");. 訂閱：張貼留言 (Atom). 溝通包容相知相惜改變著想. 圖片視窗範本. 技術提供： Blogger.

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Web Intelligence and Data Mining Laboratory. Co-clustering of multi-view datasets: a parallelizable approach. Transfer learning in heterogeneous collaborative filtering domains. A scalable collaborative filtering framework based on co clustering. Incremental Collaborative Filtering via Evolutionary Co-clustering. Using support vector machine with a hybrid feature selection method to the stock trend prediction. 訂閱：文章 (Atom). Journal of Citation Report. Co-clustering of multi-view datasets: a paralleliz.

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