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» Semi-supervised Learning from General Unlabeled Data
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ICML
2009
IEEE
14 years 9 months ago
Uncertainty sampling and transductive experimental design for active dual supervision
Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features. Labeled features are naturally available in tasks such as text c...
Vikas Sindhwani, Prem Melville, Richard D. Lawrenc...
ICML
2002
IEEE
14 years 9 months ago
IEMS - The Intelligent Email Sorter
Classification of email is an important everyday task for a large and growing number of users. This paper describes the machine learning approaches underlying the i-ems (Intellige...
Elisabeth Crawford, Judy Kay, Eric McCreath
WWW
2008
ACM
14 years 8 months ago
Why web 2.0 is good for learning and for research: principles and prototypes
The term "Web 2.0" is used to describe applications that distinguish themselves from previous generations of software by a number of principles. Existing work shows that...
Carsten Ullrich, Kerstin Borau, Heng Luo, Xiaohong...
KDD
2004
ACM
624views Data Mining» more  KDD 2004»
14 years 1 months ago
Programming the K-means clustering algorithm in SQL
Using SQL has not been considered an efficient and feasible way to implement data mining algorithms. Although this is true for many data mining, machine learning and statistical a...
Carlos Ordonez
WWW
2009
ACM
14 years 8 months ago
Enhancing diversity, coverage and balance for summarization through structure learning
Document summarization plays an increasingly important role with the exponential growth of documents on the Web. Many supervised and unsupervised approaches have been proposed to ...
Liangda Li, Ke Zhou, Gui-Rong Xue, Hongyuan Zha, Y...