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117
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WWW
2008
ACM
16 years 4 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...
209
Voted

Book
778views
17 years 1 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
147
Voted
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
16 years 4 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
128
Voted
ICDM
2009
IEEE
155views Data Mining» more  ICDM 2009»
15 years 10 months ago
Stacked Gaussian Process Learning
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
127
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COLT
2007
Springer
15 years 9 months ago
Property Testing: A Learning Theory Perspective
Property testing deals with tasks where the goal is to distinguish between the case that an object (e.g., function or graph) has a prespecified property (e.g., the function is li...
Dana Ron