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» Predicting labels for dyadic data
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AROBOTS
2011
13 years 4 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
TFS
2011
194views Education» more  TFS 2011»
13 years 4 months ago
Top-Down Induction of Fuzzy Pattern Trees
Fuzzy pattern tree induction was recently introduced as a novel machine learning method for classification. Roughly speaking, a pattern tree is a hierarchical, tree-like structur...
R. Senge, Eyke Hüllermeier
ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 4 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
KDD
2008
ACM
183views Data Mining» more  KDD 2008»
14 years 10 months ago
Knowledge transfer via multiple model local structure mapping
The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from wh...
Jing Gao, Wei Fan, Jing Jiang, Jiawei Han
NIPS
2003
13 years 11 months ago
Boosting versus Covering
We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negative) predictions are correct. In pa...
Kohei Hatano, Manfred K. Warmuth