• Journal of the Korean Society for information Management
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• v.17 no.4
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• pp.229-248
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• 2000

Among several learning algorithms for lexl calegoriration. SVM(Snpport Vsctor Machines) has been provcd to ouq~e~fotm other classifiers. Th~study e~~aluales the categarizalion ability of en SVM classifier using the ModApte split of the Reutcrs-21578 dataset. First. an experiment 1s perlormed to test a few feature wetghtlng schemes that will be used in thc calegarization tasks. Second, (he categorization periarrnances of the lulear SVM and the non-linear SVM are compared. Finally. the binary SVM classifier is expanded into a multi-class classifier and thek pcrforrnnnces are comparativcly evaluated.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.163-165
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• 2001

최근 기계학습 분야에서 커널머신을 이용한 대표적 학습기로서 Support Vector Machine(SVM)이 주목받고 있다. SVM은 통계학자인 Vapnik에 의해 제안된 것으로 통계적 학습이론에 기반 하여 뛰어난 일반화 성능을 보여준다. 그러나. SVM은 2클래tm 분류기이므로 일반적인 다중 클래스 패턴인식 문제에 적용할 수 없다. 본 논문에서는 이를 해결하기 위해 SVM을 신경망과 결합하여 다중 클래스 분류기로 확장하는 방법을 새롭게 제안한다. 제안하는 분류기의 성능을 비교하기 위하여 ORL얼굴 데이터를 이용하여 제안하는 분류기와 기존의 대표적인 다중 SVM, 신경망, PCA를 적응한 얼굴인식 실험을 수행하였다. 실험결과 제안하는 분류기를 이용한 얼굴인식률이 기존의 다중 SVM을 이용한 경우보다 3%, 신경망을 이용한 경우보다 6% 높은 수치를 보였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.27-34
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• 2005

Ventricular fibrillation(VF) is generally caused by chaotic behavior of electrical propagation in heart and may result in sudden cardiac death. In this study, we proposed a ventricular fibrillation detection algorithm based on support vector machine classifier, which could offer benefits to reduce the teaming costs as well as good classification performance. Before the extraction of input features, raw ECG signal was applied to preprocessing procedures, as like wavelet transform based bandpass filtering, R peak detection and segment assignment for feature extraction. We selected input features which of some are related to the rhythm information and of others are related to wavelet coefficients that could describe the morphology of ventricular fibrillation well. Parameters for SVM classifier, C and ${\alpha}$, were chosen as 10 and 1 respectively by trial and error experiments. Each average performance for normal sinus rhythm ventricular tachycardia and VF, was 98.39%, 96.92% and 99.88%. And, when the VF detection performance of SVM classifier was compared to that of multi-layer perceptron and fuzzy inference methods, it showed similar or higher values. Consequently, we could find that the proposed input features and SVM classifier would one of the most useful algorithm for VF detection.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.76-78
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• 2006

현재 심혈관 질환은 암 다음으로 높은 사망 원인으로 기록되고 있어 심혈관 질환에 대한 초기 진단은 질환의 치료에 매우 중요한 문제로 대두되고 있다. 본 논문에서는 SVM을 이용하여 심혈관질환 환자의 질환 단계를 예측하였다. 일반적으로 이진분류에 사용되는 SVM을 이용하여 정상 및 질환 $1{\sim}3$기의 총 4가지 분류가 필요한 다분류 분류문제를 처리하기 위해서 논문에서는 독립적 학습된 단일 SVM 분류기들을 결합하여 분류를 수행하는 SVM 앙상블 방법을 사용하였다. 단일 분류기의 결합은 Majority voting, 최소자승에러기반 가중치 부여, 2단계층 결합 등의 방법으로 수행하여 심혈관 질환 분류에 적합한 앙상블의 구성을 시도하였다. 실험 데이터는 (주)제노프라의 압타머 칩 데이터를 사용하였다. 서로 다른 데이터를 이용하여 학습된 이종의 SVM들을 결합한 결과 질환단계 예측에 있어서 단일 SVM을 이용하여 질환 단계를 예측하는 경우 보다 향상된 질환단계 예측 성능을 관찰할 수 있었으며, 심혈관 질환의 예측에 대해서는 단일 SVM 분류기의 2단 계층 결합법이 가장 좋은 성능을 보임을 확인하였다.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.84-90
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• 2010

DBMS is used for a range of applications from data warehousing through on-line transaction processing. As a result of this demand, DBMS has continued to grow in terms of its size. This growth invokes the most important issue of manually tuning the performance of DBMS. The DBMS tuning should be adaptive to the type of the workload put upon it. But, identifying workloads in mixed database applications might be quite difficult. Therefore, a method is necessary for identifying workloads in the mixed database environment. In this paper, we propose a SVM workload classifier to automatically identify a DBMS workload. Database workloads are collected in TPC-C and TPC-W benchmark while changing the resource parameters. Parameters for SVM workload classifier, C and kernel parameter, were chosen experimentally. The experiments revealed that the accuracy of the proposed SVM workload classifier is about 9% higher than that of Decision tree, Naive Bayes, Multilayer perceptron and K-NN classifier.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.883-885
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• 2005

지지 벡터 기계(Support Vector Machine: SVM)를 이용한 다중부류 분류기법이 최근 활발히 연구되고 있다. SVM은 이진분류기이기 때문에 다중부류 분류를 위해서 다수의 분류기를 구성하고 이들을 효과적으로 결합하는 방법이 필요하다. 본 논문에서는 기존의 정적인 다중분류기 결합 방법과는 달리 포섭구조의 분류모델을 확률에 따라 동적으로 구성하는 방법을 제안한다. 확률적 분류기인 나이브 베이즈 분류기(NB)를 이용하여 입력된 샘플의 각 클래스에 대한 확률을 계산하고, OVA (One-Vs-All) 전략으로 구축된 다중의 SVM을 획득된 확률에 따라 포섭구조로 구성한다. 제안하는 방법은 OVA SVM에서 발생하는 중의적인 상황을 효과적으로 처리하여 고성능의 분류를 수행한다. 본 논문에서는 지문분류 문제에서 대표적인 NIST-4 지문 데이터베이스를 대상으로 제안하는 방법을 적용하여 $1.8\%$의 거부율에서 $90.8\%$의 분류율을 획득하였으며, 기존의 결합 방법인 다수결 투표(Majority vote), 승자독식(Winner-takes-all), 행동지식공간 (Behavior knowledge space), 결정템플릿(Decision template) 등보다 높은 성능을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.549-554
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• 2008

Support Vector machine is the classifier which is based on the statistical training theory. Twin Support Vector Machine(TWSVM) is a kind of binary classifier that determines two nonparallel planes by solving two related SVM-type problems. The training time of TWSVM is shorter than that of SVM, but TWSVM doesn't shows worse performance than that of SVM. This paper proposes the TWSVM which is applied fuzzy membership, and compares the performance of this classifier with the other classifiers using Ionosphere radar data set.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.136-142
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• 2007

Recent researches in the recognition system have shown that SVM (Support Vector Machine) classifiers often have superior recognition rates in comparison to other classifiers. In this paper, we present the handwritten numeral recognition algorithm using SVM classifiers. The numeral features used in our algorithm are mesh features, directional features by Kirsch operators and concavity features, where first two features represent the foreground information of numerals and the last feature represents the background information of numerals. These features are complements each of the other. Since SVM is basically a binary classifier, it is required to construct and combine several binary SVMs to get the multi-class classifiers. We use two strategies for implementing multi-class SVM classifiers: "one against one" and "one against the rest", and examine their performances on the features used. The efficiency of our method is tested by the CENPARMI handwritten numeral database, and the recognition rate of 98.45% is achieved.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.