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» Learning Diagram Parts with Hidden Random Fields
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NIPS
2001
14 years 10 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
ICML
2003
IEEE
15 years 10 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
NIPS
2004
14 years 10 months ago
Exponential Family Harmoniums with an Application to Information Retrieval
Directed graphical models with one layer of observed random variables and one or more layers of hidden random variables have been the dominant modelling paradigm in many research ...
Max Welling, Michal Rosen-Zvi, Geoffrey E. Hinton
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ICIP
2007
IEEE
15 years 3 months ago
Propagating Image-Level Part Statistics to Enhance Object Detection
The bag-of-words approach has become increasingly attractive in the fields of object category recognition and scene classification, witnessed by some successful applications [5, 7...
Sheng Gao, Joo-Hwee Lim, Qibin Sun
ICML
2007
IEEE
15 years 10 months ago
Dynamic hierarchical Markov random fields and their application to web data extraction
Hierarchical models have been extensively studied in various domains. However, existing models assume fixed model structures or incorporate structural uncertainty generatively. In...
Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen