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JMLR
2010
121views more  JMLR 2010»
13 years 2 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
ICRA
2007
IEEE
189views Robotics» more  ICRA 2007»
14 years 1 months ago
Context Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contexts
— Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it’s use for robust pre...
Georgios Petkos, Sethu Vijayakumar
JMLR
2008
150views more  JMLR 2008»
13 years 7 months ago
Discriminative Learning of Max-Sum Classifiers
The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and obser...
Vojtech Franc, Bogdan Savchynskyy
CIKM
2008
Springer
13 years 9 months ago
To swing or not to swing: learning when (not) to advertise
Web textual advertising can be interpreted as a search problem over the corpus of ads available for display in a particular context. In contrast to conventional information retrie...
Andrei Z. Broder, Massimiliano Ciaramita, Marcus F...
KDD
2009
ACM
132views Data Mining» more  KDD 2009»
14 years 8 months ago
Learning patterns in the dynamics of biological networks
Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic network...
Chang Hun You, Lawrence B. Holder, Diane J. Cook