NDLI: Deep Twin Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Li, Dewei Tian, Yingjie Xu, Honggui
Copyright Year	2014
Abstract	We propose a novel machine learning model for classification problems, Deep Twin Support Vector Machine (DTWSVM), which combines TWSVM with deep learning ideas. TWSVM is a successful algorithm for classification problems which seeks two nonparallel hyper planes to make each hyper plane close to one class and far from the other as much as possible. And Deep Learning (DL) models have shown good ability in feature extraction and dimension reduction by constructing multi-layer network. Since the feature extraction in DL can reduce feature dimension while maintain the main information of original inputs, we consider constructing a three layer network which contains input layer, hidden layer and output layer. We use two TWSVMs in the hidden layer to extract features based on the projection principle which is derived from Multi-Layer Perceptron (MLP). The two TWSVMs will get four hyper planes by solving four convex quadratic programs. A new dataset which consists of the extracted features with four feature dimensions is produced from the hidden layer and then we can input it to the main TWSVM of the output layer to make final prediction. Similar as TWSVM, we design linear DTWSVM and nonlinear DTWSVM which have been proved to be very effective in classification problems. In numerical experiments, we have obtained 100% prediction accuracy for several datasets which is state-of-the-art performance absolutely!
Starting Page	65
Ending Page	73
File Size	465208
Page Count	9
File Format	PDF
e-ISBN	9781479942749
DOI	10.1109/ICDMW.2014.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training deep learning twin support vector machine Predictive models Feature extraction convex programming Vectors Data mining Mathematical model dimension reduction
Content Type	Text
Resource Type	Article

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