Machine Learning
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning of Ensemble Classifiers on ECG Data
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
Learning to Trade with Incremental Support Vector Regression Experts
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Incremental multiple classifier active learning for concept indexing in images and videos
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
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How to acquire new knowledge from new added training data while retaining the knowledge learned before is an important problem for incremental learning. In order to handle this problem, we propose a novel algorithm that enables support vector machines to accommodate new data, including samples that correspond to previously unseen classes, while it retains previously acquired knowledge. Furthermore, our new algorithm does not require access to previously used data during subsequent incremental learning sessions. The proposed algorithm trains a support vector machine that can output posterior probability information once an incremental batch training data is acquired. The outputs of all the resulting support vector machines are simply combined by averaging. Experiments are carried out on three benchmark datasets as well as a real world text categorization task. The experimental results indicate that the proposed algorithm is superior to the traditional incremental learning algorithm, Learn++. Due to the simplicity of the proposed algorithm, it can be used more effectively in practice.