Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Robust Real-Time Face Detection
International Journal of Computer Vision
Training conditional random fields via gradient tree boosting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A Simple and Efficient Model Pruning Method for Conditional Random Fields
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
CIGAR: concurrent and interleaving goal and activity recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Feature selection for activity recognition in multi-robot domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Fast online action recognition with efficient structured boosting
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Classification and Semantic Mapping of Urban Environments
International Journal of Robotics Research
Semi-Markov conditional random fields for accelerometer-based activity recognition
Applied Intelligence
Semi-supervised Mesh Segmentation and Labeling
Computer Graphics Forum
EEM: evolutionary ensembles model for activity recognition in Smart Homes
Applied Intelligence
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
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While conditional random fields (CRFs) have been applied successfully in a variety of domains, their training remains a challenging task. In this paper, we introduce a novel training method for CRFs, called virtual evidence boosting, which simultaneously performs feature selection and parameter estimation. To achieve this, we extend standard boosting to handle virtual evidence, where an observation can be specified as a distribution rather than a single number. This extension allows us to develop a unified framework for learning both local and compatibility features in CRFs. In experiments on synthetic data as well as real activity classification problems, our new training algorithm outperforms other training approaches including maximum likelihood, maximum pseudo-likelihood, and the most recent boosted random fields.