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» A New Discriminative Kernel From Probabilistic Models
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CVPR
2008
IEEE
16 years 1 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...
NAACL
2010
14 years 9 months ago
Painless Unsupervised Learning with Features
We show how features can easily be added to standard generative models for unsupervised learning, without requiring complex new training methods. In particular, each component mul...
Taylor Berg-Kirkpatrick, Alexandre Bouchard-C&ocir...
STOC
1993
ACM
117views Algorithms» more  STOC 1993»
15 years 3 months ago
Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Michael J. Kearns
SAB
2004
Springer
159views Optimization» more  SAB 2004»
15 years 5 months ago
Swarming Behavior Using Probabilistic Roadmap Techniques
While techniques exist for simulating swarming behaviors, these methods usually provide only simplistic navigation and planning capabilities. In this review, we explore the benefi...
O. Burçhan Bayazit, Jyh-Ming Lien, Nancy M....
JMLR
2010
152views more  JMLR 2010»
14 years 6 months ago
The SHOGUN Machine Learning Toolbox
We have developed a machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers...
Sören Sonnenburg, Gunnar Rätsch, Sebasti...