Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to variational methods for graphical models
Learning in graphical models
Discriminative, generative and imitative learning
Discriminative, generative and imitative learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
MedLDA: maximum margin supervised topic models for regression and classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cutting-plane training of structural SVMs
Machine Learning
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Maximum Entropy Discrimination Markov Networks
The Journal of Machine Learning Research
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative machine learning with structure
Discriminative machine learning with structure
A combination of topic models with max-margin learning for relation detection
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Scalable inference in max-margin topic models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Nonparametric bayesian upstream supervised multi-modal topic models
Proceedings of the 7th ACM international conference on Web search and data mining
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A supervised topic model can use side information such as ratings or labels associated with documents or images to discover more predictive low dimensional topical representations of the data. However, existing supervised topic models predominantly employ likelihood-driven objective functions for learning and inference, leaving the popular and potentially powerful max-margin principle unexploited for seeking predictive representations of data and more discriminative topic bases for the corpus. In this paper, we propose the maximum entropy discrimination latent Dirichlet allocation (MedLDA) model, which integrates the mechanism behind the max-margin prediction models (e.g., SVMs) with the mechanism behind the hierarchical Bayesian topic models (e.g., LDA) under a unified constrained optimization framework, and yields latent topical representations that are more discriminative and more suitable for prediction tasks such as document classification or regression. The principle underlying the MedLDA formalism is quite general and can be applied for jointly max-margin and maximum likelihood learning of directed or undirected topic models when supervising side information is available. Efficient variational methods for posterior inference and parameter estimation are derived and extensive empirical studies on several real data sets are also provided. Our experimental results demonstrate qualitatively and quantitatively that MedLDA could: 1) discover sparse and highly discriminative topical representations; 2) achieve state of the art prediction performance; and 3) be more efficient than existing supervised topic models, especially for classification.