Making large-scale support vector machine learning practical
Advances in kernel methods
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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
Laplace maximum margin Markov networks
Proceedings of the 25th international conference on Machine learning
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Maximum Entropy Discrimination Markov Networks
The Journal of Machine Learning Research
Holistic sentiment analysis across languages: multilingual supervised latent Dirichlet allocation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A temporal latent topic model for facial expression recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Partially labeled topic models for interpretable text mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Conditional topical coding: an efficient topic model conditioned on rich features
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking trends: incorporating term volume into temporal topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proximal Methods for Hierarchical Sparse Coding
The Journal of Machine Learning Research
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
DTTM: a discriminative temporal topic model for facial expression recognition
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Discriminative Topic Modeling Based on Manifold Learning
ACM Transactions on Knowledge Discovery from Data (TKDD)
Evaluating unsupervised learning for natural language processing tasks
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Supervising latent topic model for maximum-margin text classification and regression
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A Bayesian modeling approach to multi-dimensional sentiment distributions prediction
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Object categorization based on a supervised mean shift algorithm
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Semi-Supervised Latent Dirichlet Allocation and Its Application for Document Classification
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Finding happiest moments in a social context
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Proceedings of the 22nd international conference on World Wide Web
MedLDA: maximum margin supervised topic models
The Journal of Machine Learning Research
Tag-weighted topic model for mining semi-structured documents
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
A jointly distributed semi-supervised topic model
Neurocomputing
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Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction. We develop efficient variational methods for posterior inference and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets.