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NIPS
2008
15 years 8 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...
NIPS
2008
15 years 8 months ago
Bayesian Kernel Shaping for Learning Control
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakum...
SDM
2008
SIAM
140views Data Mining» more  SDM 2008»
15 years 8 months ago
Large-Scale Many-Class Learning
In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We invest...
Omid Madani, Michael Connor
UAI
2008
15 years 8 months ago
Learning Arithmetic Circuits
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
Daniel Lowd, Pedro Domingos
UAI
2008
15 years 8 months ago
Tightening LP Relaxations for MAP using Message Passing
Linear Programming (LP) relaxations have become powerful tools for finding the most probable (MAP) configuration in graphical models. These relaxations can be solved efficiently u...
David Sontag, Talya Meltzer, Amir Globerson, Tommi...
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