SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
Text classification in a hierarchical mixture model for small training sets
Proceedings of the tenth international conference on Information and knowledge management
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
ECUE: A Spam Filter that Uses Machine Learning to Track Concept Drift
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Combining naive bayes and n-gram language models for text classification
ECIR'03 Proceedings of the 25th European conference on IR research
Learning word vectors for sentiment analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Communications of the ACM
Efficient multilabel classification algorithms for large-scale problems in the legal domain
Semantic Processing of Legal Texts
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Ensemble of exemplar-SVMs for object detection and beyond
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Sentiment classification with supervised sequence embedding
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Statistical methods for text classification are predominantly based on the paradigm of class-based learning that associates class variables with features, discarding the instances of data after model training. This results in efficient models, but neglects the fine-grained information present in individual documents. Instance-based learning uses this information, but suffers from data sparsity with text data. In this paper, we propose a generative model called Tied Document Mixture (TDM) for extending Multinomial Naive Bayes (MNB) with mixtures of hierarchically smoothed models for documents. Alternatively, TDM can be viewed as a Kernel Density Classifier using class-smoothed Multinomial kernels. TDM is evaluated for classification accuracy on 14 different datasets for multi-label, multi-class and binary-class text classification tasks and compared to instance- and class-based learning baselines. The comparisons to MNB demonstrate a substantial improvement in accuracy as a function of available training documents per class, ranging up to average error reductions of over 26% in sentiment classification and 65% in spam classification. On average TDM is as accurate as the best discriminative classifiers, but retains the linear time complexities of instance-based learning methods, with exact algorithms for both model estimation and inference.