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» Predicting relative performance of classifiers from samples
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ICML
2010
IEEE
13 years 5 months ago
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label d...
Krzysztof Dembczynski, Weiwei Cheng, Eyke Hül...
MICCAI
2005
Springer
14 years 8 months ago
Efficient Learning by Combining Confidence-Rated Classifiers to Incorporate Unlabeled Medical Data
Abstract. In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabel...
Weijun He, Xiaolei Huang, Dimitris N. Metaxas, Xia...
DMIN
2007
186views Data Mining» more  DMIN 2007»
13 years 9 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
INFFUS
2006
71views more  INFFUS 2006»
13 years 7 months ago
Comparative implementation of two fusion schemes for multiple complementary FLIR imagery classifiers
Several classifiers for forward looking infra-red imagery are designed and implemented, and their relative performance is benchmarked on 2545 images belonging to 8 different ship ...
Pierre Valin, Francois Rhéaume, Claude Trem...
ICMLA
2009
13 years 5 months ago
Regularizing the Local Similarity Discriminant Analysis Classifier
Abstract--We investigate parameter-based and distributionbased approaches to regularizing the generative, similarity-based classifier called local similarity discriminant analysis ...
Luca Cazzanti, Maya R. Gupta