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» Properties and Benefits of Calibrated Classifiers
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PKDD
2004
Springer
92views Data Mining» more  PKDD 2004»
13 years 9 months ago
Properties and Benefits of Calibrated Classifiers
Ira Cohen, Moisés Goldszmidt
KDD
2003
ACM
180views Data Mining» more  KDD 2003»
14 years 4 months ago
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient ...
Hwanjo Yu, Jiong Yang, Jiawei Han
KDD
2007
ACM
152views Data Mining» more  KDD 2007»
14 years 4 months ago
Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers
Data mining tasks such as supervised classification can often benefit from a large training dataset. However, in many application domains, privacy concerns can hinder the construc...
Vincent Yan Fu Tan, See-Kiong Ng
ICCV
2009
IEEE
14 years 9 months ago
Detecting Interpretable and Accurate Scale-Invariant keypoints
This paper presents a novel method for detecting scale invariant keypoints. It fills a gap in the set of available methods, as it proposes a scale-selection mechanism for juncti...
Wolfgang F¨orstner, Timo Dickscheid, Falko Schind...
AUSAI
2006
Springer
13 years 8 months ago
Efficient AUC Learning Curve Calculation
Abstract. A learning curve of a performance measure provides a graphical method with many benefits for judging classifier properties. The area under the ROC curve (AUC) is a useful...
Remco R. Bouckaert