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» Learning SVMs from Sloppily Labeled Data
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LOCA
2009
Springer
14 years 13 days ago
Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
Abstract. Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling contextaware applications. As most approache...
Maja Stikic, Bernt Schiele
NIPS
2003
13 years 9 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller
PAMI
2010
122views more  PAMI 2010»
13 years 6 months ago
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
—This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available o...
Lorenzo Bruzzone, Mattia Marconcini
IJCAI
2003
13 years 9 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...