Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Supervised versus multiple instance learning: an empirical comparison
ICML '05 Proceedings of the 22nd international conference on Machine learning
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
The Journal of Machine Learning Research
Predicting Structured Data (Neural Information Processing)
Predicting Structured Data (Neural Information Processing)
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Kernel methods and the exponential family
Neurocomputing
Softmax-margin CRFs: training log-linear models with cost functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Inhibition in multiclass classification
Neural Computation
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We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs). A generalized loss function is introduced, which jointly maximizes the entropy and the margin of the solution. The CRF and SSVM emerge as special cases of our framework. The probabilistic interpretation of large margin methods reveals insights about margin and slack rescaling. Furthermore, we derive the corresponding extensions for latent variable models, in which training operates on partially observed outputs. Experimental results for multiclass, linear-chain models and multiple instance learning demonstrate that the generalized loss can improve accuracy of the resulting classifiers.