Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
RoboCup-97: Robot Soccer World Cup I
RoboCup-97: Robot Soccer World Cup I
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Scalable training of L1-regularized log-linear models
Proceedings of the 24th international conference on Machine learning
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Training conditional random fields using virtual evidence boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
Market-based dynamic task allocation using heuristically accelerated reinforcement learning
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
Action selection via learning behavior patterns in multi-robot domains
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Agent-oriented incremental team and activity recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
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In multi-robot settings, activity recognition allows a robot to respond intelligently to the other robots in its environment. Conditional random fields are temporal models that are well suited for activity recognition because they can robustly incorporate rich, non-independent features computed from sensory data. In this work, we explore feature selection in conditional random fields for activity recognition to choose which features should be included in the final model. We compare two feature selection methods, grafting, a greedy forward-selection strategy, and l1 regularization, which simultaneously smoothes the model and selects a subset of the features. We use robot data recorded during four games of the Small Size League of the RoboCup'07 robot soccer world championship to empirically compare the performance of the two feature selection algorithms in terms of accuracy of the final model, the number of features selected in the final model, and the time required to train the final model.