Email Spam Classification in a Distributed Environment

Renuka, K.; Visalakshi, P.

doi:10.5958/2249-7315.2017.00034.x

Cited by 1 publication

(1 citation statement)

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“…Support vector machine is a type of machine learning algorithm, which aims at finding one optimal hyperplane to make two kinds of training data as far as possible away from the hyperplane in the high dimensional space [1].It has great advantages of solving small sample, nonlinear and high dimensional pattern recognition problem, therefore, its been successfully applied to many aspects, such as, medical image detection [2], network information classification [3,4], pattern recognition [5], etc. Classical SVM needs to solving a Quadratic Programming Problem (QPP), which computational complexity is O(l3)(l refers to the total number of training data), so when dealing with large-scale data is restricted.…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning Algorithm of Least Square Twin KSVC

Wang

Yang

Chen

et al. 2016

MATEC Web of Conferences

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Abstract. In view of the batch implementations of standard support vector machine must be retrained from scratch every time when the training set is incremental modified, an incremental learning algorithm based on least squares twin multi-class classification support vector machine (ILST-KSVC) is proposed by solving two inverse matrix. The method will be applied on online environment to update initial data, which avoided cumbersome double counting. ILST-KSVC inherited the advantages of the basic algorithm and has some merits of Least square twin support vector machine for excellent performance on training speed and support vector classification regression for K-class's well classification accuracy. The result will be confirmed no matter in low dimension or in high dimension in UCI datasets.

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Section: Introductionmentioning

confidence: 99%