The nature of statistical learning theory
The nature of statistical learning theory
Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Technologies That Make You Smile: Adding Humor to Text-Based Applications
IEEE Intelligent Systems
On Text-based Mining with Active Learning and Background Knowledge Using SVM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Making computers laugh: investigations in automatic humor recognition
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Getting serious about the development of computational humor
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A hybrid AIS-SVM ensemble approach for text classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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Humour classification is one of the most interesting and difficult tasks in text classification. Humour is subjective by nature, yet humans are able to promptly define their preferences. Nowadays people often search for humour as a relaxing proxy to overcome stressful and demanding situations, having little or no time to search contents for such activities. Hence, we propose to aid the definition of personal models that allow the user to access humour with more confidence on the precision of his preferences. In this paper we focus on a Support Vector Machine (SVM) active learning strategy that uses specific most informative examples to improve baseline performance. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results on the proposed framework.