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ACL
2012
13 years 9 months ago
Discriminative Pronunciation Modeling: A Large-Margin, Feature-Rich Approach
We address the problem of learning the mapping between words and their possible pronunciations in terms of sub-word units. Most previous approaches have involved generative modeli...
Hao Tang, Joseph Keshet, Karen Livescu
CVPR
2004
IEEE
16 years 8 months ago
Learning Distance Functions for Image Retrieval
Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest to learn such distance functions by training binary ...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
ECCV
2010
Springer
15 years 12 months ago
Maximum Margin Distance Learning for Dynamic Texture Recognition
The range space of dynamic textures spans spatiotemporal phenomena that vary along three fundamental dimensions: spatial texture, spatial texture layout, and dynamics. By describin...
INFORMATICALT
2006
88views more  INFORMATICALT 2006»
15 years 6 months ago
Improving the Performances of Asynchronous Algorithms by Combining the Nogood Processors with the Nogood Learning Techniques
Abstract. The asynchronous techniques that exist within the programming with distributed constraints are characterized by the occurrence of the nogood values during the search for ...
Ionel Muscalagiu, Vladimir Cretu
184
Voted
KDD
2009
ACM
178views Data Mining» more  KDD 2009»
16 years 7 months ago
Constrained optimization for validation-guided conditional random field learning
Conditional random fields(CRFs) are a class of undirected graphical models which have been widely used for classifying and labeling sequence data. The training of CRFs is typicall...
Minmin Chen, Yixin Chen, Michael R. Brent, Aaron E...