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» Process modeling paradigms: an evaluation
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ACL
2009
15 years 5 months ago
Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...
Yoshimasa Tsuruoka, Jun-ichi Tsujii, Sophia Anania...
ICCBR
2009
Springer
15 years 5 months ago
Toward Modeling and Teaching Legal Case-Based Adaptation with Expert Examples
Studying examples of expert case-based adaptation could advance computational modeling but only if the examples can be succinctly represented and reliably interpreted. Supreme Cour...
Kevin D. Ashley, Collin Lynch, Niels Pinkwart, Vin...
COLING
2010
15 years 2 months ago
Latent Mixture of Discriminative Experts for Multimodal Prediction Modeling
During face-to-face conversation, people naturally integrate speech, gestures and higher level language interpretations to predict the right time to start talking or to give backc...
Derya Ozkan, Kenji Sagae, Louis-Philippe Morency
CEC
2011
IEEE
14 years 7 months ago
Comparing alternative energy functions for the HP model of protein structure prediction
Abstract—Protein structure prediction is the problem of finding the functional conformation of a protein given only its amino uence. The HP lattice model is an abstract formulat...
Mario Garza-Fabre, Eduardo Rodriguez-Tello, Gregor...
CVPR
2012
IEEE
13 years 10 months ago
Multi-target tracking by online learning of non-linear motion patterns and robust appearance models
We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous...
Bo Yang, Ram Nevatia