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» Analysis and Application of Adaptive Sampling
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SDM
2012
SIAM
235views Data Mining» more  SDM 2012»
12 years 7 days ago
Sampling Strategies to Evaluate the Performance of Unknown Predictors
The focus of this paper is on how to select a small sample of examples for labeling that can help us to evaluate many different classification models unknown at the time of sampl...
Hamed Valizadegan, Saeed Amizadeh, Milos Hauskrech...

Publication
468views
13 years 6 months ago
Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)
When appearance variation of object and its background, partial occlusion or deterioration in object images occurs, most existing visual tracking methods tend to fail in tracking ...
Zhenjun Han, Jianbin Jiao, Baochang Zhang, Qixiang...
COLT
2010
Springer
13 years 7 months ago
Robust Selective Sampling from Single and Multiple Teachers
We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of ou...
Ofer Dekel, Claudio Gentile, Karthik Sridharan
PRIB
2010
Springer
242views Bioinformatics» more  PRIB 2010»
13 years 8 months ago
Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis
Abstract. Supervised classifiers require manually labeled training samples to classify unlabeled objects. Active Learning (AL) can be used to selectively label only “ambiguous...
Scott Doyle, Anant Madabhushi
EOR
2007
165views more  EOR 2007»
13 years 9 months ago
Adaptive credit scoring with kernel learning methods
Credit scoring is a method of modelling potential risk of credit applications. Traditionally, logistic regression, linear regression and discriminant analysis are the most popular...
Yingxu Yang