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» Evaluating learning algorithms and classifiers
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MICCAI
2005
Springer
16 years 4 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...
138
Voted
WWW
2008
ACM
16 years 4 months ago
Floatcascade learning for fast imbalanced web mining
This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applicatio...
Xiaoxun Zhang, Xueying Wang, Honglei Guo, Zhili Gu...
119
Voted
IJIT
2004
15 years 5 months ago
A Fuzzy Classifier with Evolutionary Design of Ellipsoidal Decision Regions
A fuzzy classifier using multiple ellipsoids approximating decision regions for classification is to be designed in this paper. An algorithm called Gustafson-Kessel algorithm (GKA)...
Leehter Yao, Kuei-Song Weng, Cherng-Dir Huang
ECAI
2006
Springer
15 years 7 months ago
Patch Learning for Incremental Classifier Design
We present a learning algorithm for nominal data. It builds a classifier by adding iteratively a simple patch function that modifies the current classifier. Its main advantage lies...
Rudy Sicard, Thierry Artières, Eric Petit
VL
2010
IEEE
160views Visual Languages» more  VL 2010»
15 years 2 months ago
Toward End-User Debugging of Machine-Learned Classifiers
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a generat...
Todd Kulesza