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» Parallelizing Feature Selection
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CVPR
2006
IEEE
16 years 5 months ago
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Kristen Grauman, Trevor Darrell
134
Voted
CVPR
2006
IEEE
16 years 5 months ago
Accelerated Kernel Feature Analysis
A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-b...
Xianhua Jiang, Yuichi Motai, Robert R. Snapp, Xing...
117
Voted
SSPR
2004
Springer
15 years 8 months ago
Recognition of Handwritten Numerals Using a Combined Classifier with Hybrid Features
Off-line handwritten numeral recognition is a very difficult task. It is hard to achieve high recognition results using a single set of features and a single classifier, since hand...
Kyoung Min Kim, Joong Jo Park, Young Gi Song, In-C...
141
Voted
ICIAP
1999
ACM
15 years 7 months ago
Comparison of Texture Features Based on Gabor Filters
—Texture features that are based on the local power spectrum obtained by a bank of Gabor filters are compared. The features differ in the type of nonlinear post-processing which ...
Peter Kruizinga, Nicolai Petkov, Simona E. Grigore...
137
Voted
SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
15 years 4 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...