Inferring decision trees using the minimum description length principle
Information and Computation
Elements of information theory
Elements of information theory
Machine Learning
Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Second-Order Perceptron Algorithm
SIAM Journal on Computing
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Multi-class confidence weighted algorithms
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A search engine for mathematical formulae
AISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Symbolic Computation
Distribution-aware online classifiers
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Text categorization has been a popular research topic for years and has become more or less a practical technology. However, there exists little research on math topic classification. Math documents contain both textual data and math expressions. The text and math can be considered as two related but different views of a math document. The goal of online math topic classification is to automatically categorize a math document containing both mathematical expressions and textual content into an appropriate topic without the need for periodically retraining the classifier. To achieve this, it is essential to have a two-view online classification algorithm, which deals with the textual data view and the math expression view at the same time. In this paper, we propose a novel adaptive two-view online math document classifier based on the Passive Aggressive (PA) algorithm. The proposed approach is evaluated on real world math questions and answers from the Math Overflow question answering system. Compared to the baseline PA algorithm, our method's overall F-measure is improved by up to 3%. The improvement of our algorithm over the plain math expression view is almost 6%.