Machine Learning
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Optimal multimodal fusion for multimedia data analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
Active learning with statistical models
Journal of Artificial Intelligence Research
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Semi-Supervised Learning
Patent classification system using a new hybrid genetic algorithm support vector machine
Applied Soft Computing
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
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Patent classification is of great importance to effective patent analysis. Traditional manual classification suffers from the problem of low efficiency and high expense. To address this issue, an interactive patent classification algorithm based on multi-classifier fusion and active learning is proposed in this paper, which comprises the construction and update of classification model. For model construction, a sub-classifier is trained for each class of the patents by means of support vector machine. Via multi-classifier fusion, the sub-classifiers are effectively combined to acquire enhanced classifiers, based on which the classification decision can be made. For model update, active learning is used to select the most informative patents for labeling, in which dynamic batch sampling is presented to cope with the problem of redundancy in traditional batch mode. Using dynamic certainty propagation, the selected patents become more informative for active learning. By iterating model construction and update, the classification performance can be gradually refined. The interactive classification algorithm is applied to both synthetic data and patents, and its effectiveness is demonstrated by the encouraging results.