Updating the inverse of a matrix
SIAM Review
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
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
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Incremental Learning from Noisy Data
Machine Learning
Detecting Concept Drift 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
Lagrangian support vector machines
The Journal of Machine Learning Research
Incremental and Decremental Least Squares Support Vector Machine and Its Application to Drug Design
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Multicategory Proximal Support Vector Machine Classifiers
Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Adaptive simplification of solution for support vector machine
Pattern Recognition
Large Scale Classification with Support Vector Machine Algorithms
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR)
Knowledge and Information Systems
Robust ensemble learning for mining noisy data streams
Decision Support Systems
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Traditional Support Vector Machines (SVMs) based learners are commonly regarded as strong classifiers for many learning tasks. Their efficiency for large-scale high dimensional data, however, has shown to be unsatisfactory. Consequently, many alternative SVM solutions exist for large-scale and/or high dimensional data. Among them, proximal support vector machine (PSVM) is a simple but effective SVM classifier. Its incremental version (ISVM) is also available for large-scale data. Nevertheless, the computational efficiency of the ISVM for high dimensional data still needs to be improved, mainly because it requires explicit matrix inversion for updating the decision model. To solve this problem, we propose, in this paper, an inverse matrix-free incremental PSVM (IMISVM) with the following two characteristics. Firstly, IMISVM avoids explicit matrix inversion and hence derives simple formulas for updating model parameters. Secondly, IMISVM achieves faster convergence speed than ISVM. Experimental results on synthetic and real-world data sets confirm that the proposed incremental classifier outperforms ISVM.