Multiple incremental decremental learning of support vector machines

Authors:
Masayuki Karasuyama;Ichiro Takeuchi
Affiliations:
Department of Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan;Department of Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan
Venue:
IEEE Transactions on Neural Networks
Year:
2010

Citing 14
Cited 4

The nature of statistical learning theory

The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Using support vector machines for time series prediction

Advances in kernel methods
Alpha seeding for support vector machines

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate on-line support vector regression

Neural Computation
The Entire Regularization Path for the Support Vector Machine

The Journal of Machine Learning Research
Efficient Computation and Model Selection for the Support Vector Regression

Neural Computation
Considering Cost Asymmetry in Learning Classifiers

The Journal of Machine Learning Research
Incremental Support Vector Learning: Analysis, Implementation and Applications

The Journal of Machine Learning Research
An Efficient Implementation of an Active Set Method for SVMs

The Journal of Machine Learning Research
Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression

Neural Computation
An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels

IEEE Transactions on Neural Networks
Incremental training of support vector machines

IEEE Transactions on Neural Networks
A New Solution Path Algorithm in Support Vector Regression

IEEE Transactions on Neural Networks

Condensed vector machines: learning fast machine for large data

IEEE Transactions on Neural Networks
Meta-cognitive Neural Network for classification problems in a sequential learning framework

Neurocomputing
On-line principal component analysis with application to process modeling

Neurocomputing
Editors Choice Article: I2VM: Incremental import vector machines

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a multiple incremental decremental algorithm of support vector machines (SVM). In online learning, we need to update the trained model when some new observations arrive and/or some observations become obsolete. If we want to add or remove single data point, conventional single incremental decremental algorithm can be used to update the model efficiently. However, to add and/or remove multiple data points, the computational cost of current update algorithm becomes inhibitive because we need to repeatedly apply it for each data point. In this paper, we develop an extension of incremental decremental algorithm which efficiently works for simultaneous update of multiple data points. Some analyses and experimental results show that the proposed algorithm can substantially reduce the computational cost. Our approach is especially useful for online SVM learning in which we need to remove old data points and add new data points in a short amount of time.