Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Proceedings of the 24th international conference on Machine learning
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Computer Methods and Programs in Biomedicine
Editors Choice Article: I2VM: Incremental import vector machines
Image and Vision Computing
An incremental approach to support vector machine learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Support Vector Machines (SVMs) have gained outstanding generalization in many fields. However, standard SVM and most modified SVMs are in essence batch learning, which makes them unable to handle incremental learning well. Also, such SVMs are not able to handle large-scale data effectively because they are costly in terms of memory and computing consumption. In some situations, plenty of Support Vectors (SVs) are produced, which generally means a long testing time. In this paper, we propose an online incremental learning SVM for large data sets. The proposed method mainly consists of two components, Learning Prototypes (LPs) and Learning SVs (LSVs). Experimental results demonstrate that the proposed algorithm is effective for incremental learning problems and large-scale problems.