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» Learning with Constrained and Unlabelled Data
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NIPS
2001
13 years 11 months ago
Covariance Kernels from Bayesian Generative Models
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
Matthias Seeger
NIPS
2007
13 years 11 months ago
Discriminative Batch Mode Active Learning
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effort needed to learn a good classifier. Most previous studies in active learning...
Yuhong Guo, Dale Schuurmans
CVPR
2008
IEEE
13 years 10 months ago
Joint learning and dictionary construction for pattern recognition
We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier p...
Duc-Son Pham, Svetha Venkatesh
ACL
2009
13 years 8 months ago
A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge
Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The prolif...
Tao Li, Yi Zhang 0005, Vikas Sindhwani
PERCOM
2005
ACM
14 years 9 months ago
Reducing the Calibration Effort for Location Estimation Using Unlabeled Samples
WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in today's pervasive computing applications. As alternative to the wellestablished...
Xiaoyong Chai, Qiang Yang