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178
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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
16 years 7 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
199
Voted
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
16 years 7 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
182
Voted
KDD
2008
ACM
146views Data Mining» more  KDD 2008»
16 years 7 months ago
Spotting out emerging artists using geo-aware analysis of P2P query strings
Record label companies would like to identify potential artists as early as possible in their careers, before other companies approach the artists with competing contracts. The va...
Noam Koenigstein, Yuval Shavitt, Tomer Tankel
KDD
2007
ACM
197views Data Mining» more  KDD 2007»
16 years 7 months ago
Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming
The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kerne...
Jieping Ye, Shuiwang Ji, Jianhui Chen
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
16 years 7 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
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