Machine Learning
Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
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
Multi-class confidence weighted algorithms
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Distribution-aware online classifiers
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Adaptive two-view online learning for math topic classification
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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We propose a two-view online learning algorithm that utilizes two different views of the same data to achieve something that is greater than the sum of its parts. Our algorithm is an extension of the single-view Passive Aggressive (PA) algorithm, where we minimize the changes in the two view weights and disagreements between the two classifiers. The final classifier is an equally weighted sum of the individual classifiers. As a result, disagreements between the two views are tolerated as long as the final combined classifier output is not compromised. Our approach thus allows the stronger voice (view) to dominate whenever the two views disagree. This additional allowance of diversity between the two views is what gives our approach the edge, as espoused by classical ensemble learning theory. Our algorithm is evaluated and compared to the original PA algorithm on three datasets. The experimental results show that it consistently outperforms the PA algorithm on individual views and concatenated view by up to 3%.