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ICML
2000
IEEE
16 years 5 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
ICML
1997
IEEE
16 years 5 months ago
Characterizing the generalization performance of model selection strategies
Abstract: We investigate the structure of model selection problems via the bias/variance decomposition. In particular, we characterize the essential structure of a model selection ...
Dale Schuurmans, Lyle H. Ungar, Dean P. Foster
CEC
2009
IEEE
15 years 11 months ago
Evolving hypernetwork models of binary time series for forecasting price movements on stock markets
— The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order...
Elena Bautu, Sun Kim, Andrei Bautu, Henri Luchian,...
MM
2009
ACM
159views Multimedia» more  MM 2009»
15 years 11 months ago
Visual categorization with negative examples for free
Automatic visual categorization is critically dependent on labeled examples for supervised learning. As an alternative to traditional expert labeling, social-tagged multimedia is ...
Xirong Li, Cees G. M. Snoek
EMMCVPR
2005
Springer
15 years 10 months ago
Object Categorization by Compositional Graphical Models
This contribution proposes a compositionality architecture for visual object categorization, i.e., learning and recognizing multiple visual object classes in unsegmented, cluttered...
Björn Ommer, Joachim M. Buhmann