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» Evaluating learning algorithms and classifiers
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ICPR
2008
IEEE
15 years 11 months ago
Learning visual dictionaries and decision lists for object recognition
Visual dictionaries are widely employed in object recognition to map unordered bags of local region descriptors into feature vectors for image classification. Most visual dictiona...
Wei Zhang, Thomas G. Dietterich
161
Voted
JMLR
2010
230views more  JMLR 2010»
14 years 11 months ago
Learning Dissimilarities for Categorical Symbols
In this paper we learn a dissimilarity measure for categorical data, for effective classification of the data points. Each categorical feature (with values taken from a finite set...
Jierui Xie, Boleslaw K. Szymanski, Mohammed J. Zak...
CVPR
2007
IEEE
16 years 6 months ago
Multiple Instance Learning of Pulmonary Embolism Detection with Geodesic Distance along Vascular Structure
We propose a novel classification approach for automatically detecting pulmonary embolism (PE) from computedtomography-angiography images. Unlike most existing approaches that req...
Jinbo Bi, Jianming Liang
145
Voted
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
ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 4 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....