The nature of statistical learning theory
The nature of statistical learning theory
A framework for structural risk minimisation
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
International Journal of Computer Vision
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Cost-sensitive learning with conditional Markov networks
Data Mining and Knowledge Discovery
Semantic object classes in video: A high-definition ground truth database
Pattern Recognition Letters
Action categorization with modified hidden conditional random field
Pattern Recognition
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Man-made structure detection in natural images using a causal multiscale random field
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Information Theory
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We tackle the structured output classification problem using the Conditional Random Fields (CRFs). Unlike the standard 0/1 loss case, we consider a cost-sensitive learning setting where we are given a non-0/1 misclassification cost matrix at the individual output level. Although the task of cost-sensitive classification has many interesting practical applications that retain domain-specific scales in the output space (e.g., hierarchical or ordinal scale), most CRF learning algorithms are unable to effectively deal with the cost-sensitive scenarios as they merely assume a nominal scale (hence 0/1 loss) in the output space. In this paper, we incorporate the cost-sensitive loss into the large margin learning framework. By large margin learning, the proposed algorithm inherits most benefits from the SVM-like margin-based classifiers, such as the provable generalization error bounds. Moreover, the soft-max approximation employed in our approach yields a convex optimization similar to the standard CRF learning with only slight modification in the potential functions. We also provide the theoretical cost-sensitive generalization error bound. We demonstrate the improved prediction performance of the proposed method over the existing approaches in a diverse set of sequence/image structured prediction problems that often arise in pattern recognition and computer vision domains.