Incremental Support Vector Machine Algorithm Research Articles

A Bayesian beta kernel model for binary classification and online learning problems

Recent advances in data mining have integrated kernel functions with Bayesian probabilistic analysis of Gaussian distributions. These machine learning approaches can incorporate prior information with new data to calculate probabilistic rather than deterministic values for unknown parameters. This paper extensively analyzes a specic Bayesian kernel model that uses a kernel function to calculate a posterior beta distribution that is conjugate to the prior beta distribution. Numerical testing of the beta kernel model on several benchmark data sets reveals that this model’s accuracy is comparable with those of the support vector machine, relevance vector machine, naive Bayes, and logistic regression, and the model runs more quickly than other algorithms. When one class occurs much more frequently than the other class, the beta kernel model often outperforms other strategies to handle imbalanced data sets, including undersampling, over-sampling, and the Synthetic Minority Over-Sampling Technique. If data arrive sequentially over time, the beta kernel model easily and quickly updates the probability distribution, and this model is more accurate than an incremental support vector machine algorithm for online learning.

A New Incremental Support Vector Machine Algorithm

Support vector machine is a popular method in machine learning. Incremental support vector machine algorithm is ideal selection in the face of large learning data set. In this paper a new incremental support vector machine learning algorithm is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vector machine. The goal concept is updated by incremental learning. Each training procedure only includes new training data. The time complexity is independent of whole training set. Compared with the other incremental version, the training speed of this approach is improved and the change of hyperplane is reduced.

Incremental support vector machine algorithm based on multi-kernel learning

Abstract: A new incremental support vector machine (SVM) al-gorithm is proposed which is based on multiple kernel learning.Through introducing multiple kernel learning into the SVM incre-mental learning, large scale data set learning problem can besolved effectively. Furthermore, different punishments are adoptedin allusion to the training subset and the acquired support vectors,which may help to improve the performance of SVM. Simulationresults indicate that the proposed algorithm can not only solve themodel selection problem in SVM incremental learning, but alsoimprove the classification or prediction precision. Keywords: support vector machine (SVM), incremental learning,multiple kernel learning (MKL). DOI: 10.3969/j.issn.1004-4132.2011.04.021 1. Introduction Supportvectormachines(SVMs),proposedbyVapnik[1],areabletotranslatethenonlinearproblemintolinearprob-lemandobtaintheglobaloptimalsolutionbyfollowingthestructure risk minimization and integrating manifold tech-niquessuchasmaximalmarginhyper-plane,Mercerkerneland slack variables. In recent years, SVMs are extensivelyapplied to pattern recognition, system modeling, time se-ries predictionand otherfields [2,3].Unfortunately, SVMs do not scale well with respectto the size of training data. Given