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IJCAI
2003
13 years 10 months ago
A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.11b network. Our approach uses data labeled by ground trut...
Sebastian Thrun, Geoffrey J. Gordon, Frank Pfennin...
NIPS
1998
13 years 10 months ago
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
This paper presents probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored...
Shumeet Baluja
ALT
1998
Springer
14 years 25 days ago
PAC Learning from Positive Statistical Queries
Learning from positive examples occurs very frequently in natural learning. The PAC learning model of Valiant takes many features of natural learning into account, but in most case...
François Denis
COLING
2010
13 years 3 months ago
Improving Name Origin Recognition with Context Features and Unlabelled Data
We demonstrate the use of context features, namely, names of places, and unlabelled data for the detection of personal name language of origin. While some early work used either r...
Vladimir Pervouchine, Min Zhang, Ming Liu, Haizhou...
ICASSP
2009
IEEE
14 years 3 months ago
Using collective information in semi-supervised learning for speech recognition
Training accurate acoustic models typically requires a large amount of transcribed data, which can be expensive to obtain. In this paper, we describe a novel semi-supervised learn...
Balakrishnan Varadarajan, Dong Yu, Li Deng, Alex A...