• 응용통계연구
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• 제18권2호
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• pp.395-410
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• 2005

많은 수의 범주형 변수에 의한 대규모 분할표 분석을 위하여 차원축소(collapsibility) 성질을 이용한 분석 방법을 제안하였다. kullback-Leibler의 발산 측도(divergence measure)를 이용한 서로 완전한 연관을 갖는 변수그룹을 결정하는 방법을 제안하였으며, 제안된 방법에 의한 변수그룹은 주변 로그선형모형(marginal log-linear models)에 의하여 변수들간의 연관성을 식별할 수 있다. 제안된 방법의 적용 예로 데이터 마이닝에서 흔히 접할 수 있는 대규모 분할표 자료인 소비자들의 구매행위 분석을 위한 장바구니 자료의 분석 결과를 제시하였다.

• International Journal of Contents
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• 제5권4호
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• pp.51-54
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• 2009

In huge video databases, the effective video content indexing method is required. While manual indexing is the most effective approach to this goal, it is slow and expensive. Thus automatic indexing is desirable and recently various indexing tools for video databases have been developed. For efficient video content indexing, the similarity measure is an important factor. This paper presents new similarity measures between frames and proposes a new algorithm to index video content using Kullback-Leibler distance defined between two histograms. Experimental results show that the proposed algorithm using Kullback-Leibler distance gives remarkable high accuracy ratios compared with several conventional algorithms to index video content.

• Journal of the Korean Statistical Society
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• 제29권3호
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• pp.307-313
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• 2000

Suppose we have a set of data X1, ···, Xn and employ kernel density estimator to estimate the marginal density of X. in this article bandwith selection problem for kernel density estimator is examined closely. In particular the Kullback-Leibler method (a bandwith selection methods based on average square error (ASE)) is considered.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권4호
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• pp.152-159
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• 2002

Fitness sharing that shares fitness if calculated distance between individuals is smaller than sharing radius is one of the representative speciation methods and can complement evolutionary algorithm which converges one solution. Recently, there are many researches on designing neural network architecture using evolutionary algorithm but most of them use only the fittest solution in the last generation. In this paper, we elaborate generating diverse neural networks using fitness sharing and combing them to compute outputs then, propose calculating distance between individuals using modified Kullback-Leibler entropy for improvement of fitness sharing performance. In the experiment of Australian credit card assessment, breast cancer, and diabetes in UCI database, proposed method performs better than not only simple average output or Pearson Correlation but also previous published methods.

• The Journal of the Acoustical Society of Korea
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• 제27권2E호
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• pp.57-63
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• 2008

Stethoscopic auscultation is still one of the primary tools for the diagnosis of heart diseases due to its easy accessibility and relatively low cost. It is, however, a difficult skill to acquire. Many research efforts have been done on the automatic classification of heart sound signals to support clinicians in heart sound diagnosis. Recently, hidden Markov models (HMMs) have been used quite successfully in the automatic classification of the heart sound signal. However, in the classification using HMMs, there are so many heart sound signal types that it is not reasonable to assign a new class to each of them. In this paper, rather than constructing an HMM for each signal type, we propose to build an HMM for a set of acoustically-similar signal types. To define the classes, we use the KL (Kullback-Leibler) distance between different signal types to determine if they should belong to the same class. From the classification experiments on the heart sound data consisting of 25 different types of signals, the proposed method proved to be quite efficient in determining the optimal set of classes. Also we found that the class determination approach produced better results than the heuristic class assignment method.

• 한국정보과학회논문지:데이타베이스
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• 제37권6호
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• pp.285-291
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• 2010

본 연구에서는 나이브 베이시안 학습의 환경에서 속성의 가중치를 계산하는 새로운 방식을 제안한다. 기존 방법들이 속성에 가중치를 부여하는 방식인데 반하여 본 연구에서는 한걸음 더 나아가 속성의 값에 가중치를 부여하는 새로운 방식을 연구하였다. 이러한 속성값의 가중치를 계산하기 위하여 Kullback-Leibler 함수를 이용하여 가중치를 계산하는 방식을 제안하였고 이러한 가중치들의 특성을 분석하였다. 제안된 알고리즘은 다수의 데이터를 이용하여 속성 가중치 방식과 비교하였고 대부분의 경우에 더 좋은 성능을 제공함을 알 수 있었다.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제4권1호
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• pp.97-104
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• 1997

The large deviations theorem of Cramer is extended to conditional probabilities in the following sense. Consider a random sample of pairs of random vectors and the sample means of each of the pairs. The probability that the first falls outside a certain convex set given that the second is fixed is shown to decrease with the sample size at an exponential rate which depends on the Kullback-Leibler distance between two distributions in an associated exponential familiy of distributions. Examples are given which include a method of computing the Bahadur exact slope for tests of certain composite hypotheses in exponential families.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.195-203
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• 1996

A bootstrap method is used to correct the apparent downward bias of a naive plug-in bootstrap model selection criterion, which is shown to enjoy a high degree of accuracy. Comparison of bootstrap method with the asymptotic method is made through an illustrative example.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권5호
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• pp.1463-1478
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• 2012

Based on the theory of local-world network, the composition self-similarity (CSS) of network traffic is presented for the first time in this paper for the study of DoS detection. We propose the concept of composition distribution graph and design the relative operations. The $(R/S)^d$ algorithm is designed for calculating the Hurst parameter. Based on composition distribution graph and Kullback Leibler (KL) divergence, we propose the composition self-similarity anomaly detection (CSSD) method for the detection of DoS attacks. We evaluate the effectiveness of the proposed method. Compared to other entropy based anomaly detection methods, our method is more accurate and with higher sensitivity in the detection of DoS attacks.

• 대한전자공학회논문지SP
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• 제41권6호
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• pp.89-97
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• 2004

빛을 반사하거나 방출하는 물체의 형상은 여러 원인에 의해 왜곡된 영상으로 센서에 포착된다. 이러한 왜곡을 제거해 원래 물체의 형상을 추정하는 것을 영상 복원이라고 한다. 영상 복원은 결정론적 방법과 확률론적 방법이 있다. 본 논문에서는 확률론적 방법의 한 종류로서 피셔 인포메이션(Fisher Information)으로부터 유도된 MFI(Minimum Fisher Information)을 이용한 영상 복원을 제안한다. 이는 B. Roy Frieden에 의해 최근에 제안된 신호 추정 방법의 하나이다. MFI을 이용한 복원에서 노이즈 제어 파라미터에 따라 영상 복원의 결과가 어떻게 변화하는지를 조사하였으며, 복원의 정확도에 대한 기준으로 크로스 엔트로피(Kullback-Leibler entropy)를 사용하였다.