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» Learning Instance-Specific Predictive Models
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ICML
2005
IEEE
14 years 9 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III
PUC
2002
98views more  PUC 2002»
13 years 8 months ago
A User-Centered Location Model
: This paper discusses the user-centered location model used in comMotion. In this context, the location model refers to a set of learned places (destinations), which coincide to a...
Natalia Marmasse, Chris Schmandt
TKDE
2012
190views Formal Methods» more  TKDE 2012»
11 years 11 months ago
Scalable Learning of Collective Behavior
—This study of collective behavior is to understand how individuals behave in a social networking environment. Oceans of data generated by social media like Facebook, Twitter, Fl...
Lei Tang, Xufei Wang, Huan Liu
NECO
2002
104views more  NECO 2002»
13 years 8 months ago
An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models
A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
Harri Valpola, Juha Karhunen
NAACL
2010
13 years 6 months ago
Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
We describe a method of incorporating taskspecific cost functions into standard conditional log-likelihood (CLL) training of linear structured prediction models. Recently introduc...
Kevin Gimpel, Noah A. Smith