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» Learning from Highly Structured Data by Decomposition
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CVPR
2005
IEEE
16 years 6 months ago
Learning a Similarity Metric Discriminatively, with Application to Face Verification
We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large an...
Sumit Chopra, Raia Hadsell, Yann LeCun
IVC
2007
164views more  IVC 2007»
15 years 4 months ago
Locality preserving CCA with applications to data visualization and pose estimation
- Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality reduction and has been applied to image processing, pose estimation and other fields. H...
Tingkai Sun, Songcan Chen
DMIN
2007
186views Data Mining» more  DMIN 2007»
15 years 6 months ago
Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?
- The classifier built from a data set with a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurr...
Gary M. Weiss, Kate McCarthy, Bibi Zabar
AAAI
1996
15 years 5 months ago
Post-Analysis of Learned Rules
Rule induction research implicitly assumes that after producing the rules from a dataset, these rules will be used directly by an expert system or a human user. In real-life appli...
Bing Liu, Wynne Hsu
125
Voted
CVPR
2010
IEEE
16 years 28 days ago
Unsupervised Learning of Invariant Features Using Video
We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without hu...
David Stavens, Sebastian Thrun