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» Detecting Fractures in Classifier Performance
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KDD
2004
ACM
196views Data Mining» more  KDD 2004»
15 years 10 months ago
Adversarial classification
Essentially all data mining algorithms assume that the datagenerating process is independent of the data miner's activities. However, in many domains, including spam detectio...
Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. ...
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
15 years 10 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
NIPS
2001
14 years 11 months ago
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed (e.g. face detection or databa...
Paul A. Viola, Michael J. Jones
BMVC
2010
14 years 4 months ago
Saliency Segmentation based on Learning and Graph Cut Refinement
Saliency detection is a well researched problem in computer vision. In previous work, most of the effort is spent on manually devising a saliency measure. Instead we propose a sim...
Paria Mehrani, Olga Veksler
KDD
2005
ACM
117views Data Mining» more  KDD 2005»
15 years 10 months ago
Rule extraction from linear support vector machines
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
Glenn Fung, Sathyakama Sandilya, R. Bharat Rao