• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.655-660
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• 2003

In general, Support Vector Machine has a good performance in binary classification, but it has the limitation on multi-pattern classification. So, we proposed an Import Vector Voting model for two or more labels classification. This model applied kernel bagging strategy to Import Vector Machine by Zhu. The proposed model used a voting strategy which averaged optimal kernel function from many kernel functions. In experiments, not only binary but multi-pattern classification problems, our proposed Import Vector Voting model showed good performance for given machine learning data.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.189-195
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• 2012

In this paper, face detection method in NIR(Near-InfraRed) images for human recognition is proposed. Edge histogram based on edge intensity and its direction, has been used to detect effectively faces on NIR image. The edge histogram descripts and discriminates face effectively because it is strong in environment of lighting change. SVM(Support Vector Machine) has been used as a classifier to detect face and the proposed method showed better performance with smaller features than in ULBP(Uniform Local Binary Pattern) based method.

• Journal of the Semiconductor & Display Technology
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• v.8 no.3
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• pp.39-44
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• 2009

Local Binary Pattern (LBP) is becoming a popular tool for various machine vision applications such as face recognition, classification and background subtraction. In this paper, we propose a new extension of LBP, called the Diagonal LBP (DLBP), to handle the image-based gender classification problem arise in interactive display systems. Instead of comparing neighbor pixels with the center pixel, DLBP generates codes by comparing a neighbor pixel with the diagonal pixel (the neighbor pixel in the opposite side). It can reduce by half the code length of LBP and consequently, can improve the computation complexity. The Support Vector Machine is utilized as the gender classifier, and the texture profile based on DLBP is adopted as the feature vector. Experimental results revealed that our approach based on the diagonal LPB is very efficient and can be utilized in various real-time pattern classification applications.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• Journal of Korea Multimedia Society
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• v.11 no.7
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• pp.1035-1042
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• 2008

The support vector machine, as a binary classifier, is known to surpass the other classifiers only in binary classification problems through the various experiments. Even though its theory is based on the maximal margin classifier, the support vector machine approach cannot be easily extended to the multi-classification problems. In this paper, we review the extension techniques of the support vector machine toward the multi-classification and do the performance comparison. Depending on the data decomposition of the training data, the support vector machine is easily adapted for a multi-classification problem without modifying the intrinsic characteristics of the binary classifier. The performance is evaluated on a collection of the benchmark data sets and compared according to the selected teaming strategies, the training time, and the results of the neural network with the backpropagation teaming. The experiments suggest that the support vector machine is applicable and effective in the general multi-class classification problems when compared to the results of the neural network.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.83-93
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• 2001

We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.

• Speech Sciences
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• v.8 no.3
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• pp.143-148
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• 2001

We propose a new scheme for VQ codebook search. The procedure is in between the binary-tree-search and full-search and thus might be called N-ary search of a codebook. Through the experiment performed on 7200 frames spoken by 25 speakers, we confirmed that the best codewords as good as by the full-search were obtained at moderate time consumption comparable to the binary-tree-search. In application to speech recognition by HMM/VQ with Bakis model, where appearance of a specific codeword is essential in the parameter training phase, the method proposed here is expected to provide an efficient training procedure.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.18-21
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• 1999

This paper presents a speed and position sensorless control of permanent magnet synchronous motors using an integral binary observer. In order to improve the steady state performance of the binary observer, the binary observer is formed by adding extra integral dynamics to the switching hyperplane equation. The observer structure and its deign method are described. The experimetntal results of the proposed algorithm are presented to demonstrate the effectiveness of the approach.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.795-801
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• 2013

Recently, support vector machine has been widely used in various application fields due to its superiority of classification performance comparing with decision tree and neural network. Since support vector machine is basically designed for the binary classification problem, output coding method to analyze the classification result of multiclass binary classifier is used for the application of support vector machine into the multiclass problem. However, previous feature selection method for output coding based support vector machine found the features to improve the overall classification accuracy instead of improving each classification accuracy of each classifier. In this paper, we propose the novel feature selection method to find the features for maximizing the classification accuracy of each binary classifier in output coding based support vector machine. Experimental result showed that proposed method significantly improved the classification accuracy comparing with previous feature selection method.