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» Approximation Methods for Supervised Learning
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CIKM
2009
Springer
14 years 4 months ago
Combining labeled and unlabeled data with word-class distribution learning
We describe a novel simple and highly scalable semi-supervised method called Word-Class Distribution Learning (WCDL), and apply it the task of information extraction (IE) by utili...
Yanjun Qi, Ronan Collobert, Pavel Kuksa, Koray Kav...
CVPR
2012
IEEE
12 years 12 days ago
Large scale metric learning from equivalence constraints
In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization p...
Martin Köstinger, Martin Hirzer, Paul Wohlhar...
ASC
2007
13 years 10 months ago
An approximate stability analysis of nonlinear systems described by Universal Learning Networks
Stability is one of the most important subjects in control systems. As for the stability of nonlinear dynamical systems, Lyapunov’s direct method and linearized stability analys...
Kotaro Hirasawa, Shingo Mabu, Shinji Eto, Jinglu H...
SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
13 years 24 days ago
Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
James R. Foulds, Padhraic Smyth
NIPS
1998
13 years 11 months ago
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy
We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line le...
Yoram Singer, Manfred K. Warmuth