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» Introduction to Pattern Recognition
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KDD
2012
ACM
281views Data Mining» more  KDD 2012»
13 years 6 months ago
Active spectral clustering via iterative uncertainty reduction
Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straightforw...
Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jord...
199
Voted
CVPR
2012
IEEE
13 years 6 months ago
Fixed-rank representation for unsupervised visual learning
Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-theart technique...
Risheng Liu, Zhouchen Lin, Fernando De la Torre, Z...
158
Voted
AAAI
2012
13 years 6 months ago
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping
Selecting the optimal kernel is an important and difficult challenge in applying kernel methods to pattern recognition. To address this challenge, multiple kernel learning (MKL) ...
Tomer Levinboim, Fei Sha
164
Voted
PR
2006
167views more  PR 2006»
15 years 3 months ago
Database, protocols and tools for evaluating score-level fusion algorithms in biometric authentication
Fusing the scores of several biometric systems is a very promising approach to improve the overall system's accuracy. Despite many works in the literature, it is surprising t...
Norman Poh, Samy Bengio
132
Voted
GECCO
2007
Springer
194views Optimization» more  GECCO 2007»
15 years 9 months ago
Hybrid coevolutionary algorithms vs. SVM algorithms
As a learning method support vector machine is regarded as one of the best classifiers with a strong mathematical foundation. On the other hand, evolutionary computational techniq...
Rui Li, Bir Bhanu, Krzysztof Krawiec