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» On learning algorithm selection for classification
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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 7 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
CIKM
2005
Springer
16 years 5 days ago
A novel refinement approach for text categorization
In this paper we present a novel strategy, DragPushing, for improving the performance of text classifiers. The strategy is generic and takes advantage of training errors to succes...
Songbo Tan, Xueqi Cheng, Moustafa Ghanem, Bin Wang...
MICCAI
2008
Springer
16 years 7 months ago
A Bayesian Approach for Liver Analysis: Algorithm and Validation Study
Abstract. We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and metastatic lesions from abdominal CTA scans. The method repeat...
Moti Freiman, Ofer Eliassaf, Yoav Taieb, Leo Jo...
CIKM
2009
Springer
15 years 10 months ago
Efficient feature weighting methods for ranking
Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generali...
Hwanjo Yu, Jinoh Oh, Wook-Shin Han
ICIP
2006
IEEE
16 years 8 months ago
Two-Stage Optimal Component Analysis
Linear techniques are widely used to reduce the dimension of image representation spaces in applications such as image indexing and object recognition. Optimal Component Analysis ...
Yiming Wu, Xiuwen Liu, Washington Mio, Kyle A. Gal...