The nature of statistical learning theory
The nature of statistical learning theory
Pairwise classification and support vector machines
Advances in kernel methods
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Optimization on Support Vector Machines
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
An approach to incremental SVM learning algorithm
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
Incremental training of support vector machines
IEEE Transactions on Neural Networks
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Online error correcting output codes
Pattern Recognition Letters
Electromechanical equipment state forecasting based on genetic algorithm - support vector regression
Expert Systems with Applications: An International Journal
A DIAMOND method of inducing classification rules for biological data
Computers in Biology and Medicine
An incremental ensemble of classifiers
Artificial Intelligence Review
Training support vector machines on large sets of image data
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
A DIAMOND method for classifying biological data
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Incremental training of support vector machines using truncated hypercones
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
A note on hyper ellipse method for classifying biological and medical data
Computers in Biology and Medicine
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In the conventional incremental training of support vector machines, candidates for support vectors tend to be deleted if the separating hyperplane rotates as the training data are added. To solve this problem, in this paper, we propose an incremental training method using one-class support vector machines. First, we generate a hypersphere for each class. Then, we keep data that exist near the boundary of the hypersphere as candidates for support vectors and delete others. By computer simulations for two-class and multiclass benchmark data sets, we show that we can delete data considerably without deteriorating the generalization ability.