Neural Networks - 2005 Special issue: IJCNN 2005
Incremental training of support vector machines using hyperspheres
Pattern Recognition Letters
Face detection in gray scale images using locally linear embeddings
Computer Vision and Image Understanding
Collaborative Target Classification for Image Recognition in Wireless Sensor Networks
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
An Incremental Feature Learning Algorithm Based on Least Square Support Vector Machine
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Gaussian process approach for modelling of nonlinear systems
Engineering Applications of Artificial Intelligence
Recursive Bayesian linear regression for adaptive classification
IEEE Transactions on Signal Processing
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Distributed visual-target-surveillance system in wireless sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
An efficient active set method for SVM training without singular inner problems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An effective method of pruning support vector machine classifiers
IEEE Transactions on Neural Networks
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
Convergence improvement of active set training for support vector regressors
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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
Journal of Biomedical Informatics
Detection of tornados using an incremental revised support vector machine with filters
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Finding faces in gray scale images using locally linear embeddings
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
A dynamic model selection strategy for support vector machine classifiers
Applied Soft 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
Locally training the log-linear model for SMT
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Hierarchical training of multiple SVMs for personalized web filtering
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Fast pruning superfluous support vectors in SVMs
Pattern Recognition Letters
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
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We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.