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NIPS
2004
13 years 10 months ago
Support Vector Classification with Input Data Uncertainty
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the stati...
Jinbo Bi, Tong Zhang
BMCBI
2010
126views more  BMCBI 2010»
13 years 8 months ago
A boosting method for maximizing the partial area under the ROC curve
Background: The receiver operating characteristic (ROC) curve is a fundamental tool to assess the discriminant performance for not only a single marker but also a score function c...
Osamu Komori, Shinto Eguchi
ACL
1997
13 years 10 months ago
Document Classification Using a Finite Mixture Model
We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of cla...
Hang Li, Kenji Yamanishi
STOC
1993
ACM
117views Algorithms» more  STOC 1993»
14 years 21 days ago
Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Michael J. Kearns
CVPR
2009
IEEE
15 years 3 months ago
Efficient Scale Space Auto-Context for Image Segmentation and Labeling
The Conditional Random Fields (CRF) model, using patch-based classification bound with context information, has recently been widely adopted for image segmentation/ labeling. In...
Jiayan Jiang (UCLA), Zhuowen Tu (UCLA)