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» Results of the KDD'99 Classifier Learning
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KDD
2006
ACM
180views Data Mining» more  KDD 2006»
16 years 1 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
ECCV
2008
Springer
16 years 3 months ago
Face Alignment Via Component-Based Discriminative Search
In this paper, we propose a component-based discriminative approach for face alignment without requiring initialization1 . Unlike many approaches which locally optimize in a small ...
Lin Liang, Rong Xiao, Fang Wen, Jian Sun
ICML
1998
IEEE
16 years 2 months ago
Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting
In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes a...
Moisés Goldszmidt, Nir Friedman, Thomas J. ...
SEKE
2009
Springer
15 years 8 months ago
Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning
Code review is often suggested as a means of improving code quality. Since humans are poor at repetitive tasks, some form of tool support is valuable. To that end we developed a p...
Stefan Axelsson, Dejan Baca, Robert Feldt, Darius ...
CVPR
2009
IEEE
16 years 8 months ago
Learning Invariant Features Through Topographic Filter Maps
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergu...