• 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.

• 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.

• Communications for Statistical Applications and Methods
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• 제29권4호
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• pp.471-486
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• 2022

Autoregressive moving average (ARMA) models involve nonlinearity in the model coefficients because of unobserved lagged errors, which complicates the likelihood function and makes the posterior density analytically intractable. In order to overcome this problem of posterior analysis, some approximation methods have been proposed in literature. In this paper we first review the main analytic approximations proposed to approximate the posterior density of ARMA models to be analytically tractable, which include Newbold, Zellner-Reynolds, and Broemeling-Shaarawy approximations. We then use the Kullback-Leibler divergence to study the relation between these three analytic approximations and to measure the distance between their derived approximate posteriors for ARMA models. In addition, we evaluate the impact of the approximate posteriors distance in Bayesian estimates of mean and precision of the model coefficients by generating a large number of Monte Carlo simulations from the approximate posteriors. Simulation study results show that the approximate posteriors of Newbold and Zellner-Reynolds are very close to each other, and their estimates have higher precision compared to those of Broemeling-Shaarawy approximation. Same results are obtained from the application to real-world time series datasets.

• Kyungpook Mathematical Journal
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• 제62권2호
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• pp.389-405
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• 2022

We study the Fisher Information (FI) of m-generalized order statistics (m-GOSs) and their concomitants about the shape-parameter vector of the Iterated Farlie-Gumbel-Morgenstern (IFGM) bivariate distribution. We carry out a computational study and show how the FI matrix (FIM) helps in finding information contained in singly or multiply censored bivariate samples from the IFGM. We also run numerical computations about the FIM for the sub-models of order statistics (OSs) and sequential order statistics (SOSs). We evaluate FI about the mean and the shape-parameter of exponential and power distributions, respectively. Finally, we investigate the Kullback-Leibler distance in concomitants of m-GOSs.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제7권1호
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• pp.63-77
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• 2003

Some inequalities for the $Csisz{\acute{a}}r\;{\Phi}-divergence$ and applications for the Kullback-Leibler, $R{\acute{e}}nyi$, Hellinger and Bhattacharyya distances in Information Theory are given.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1223-1229
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• 2012

In this paper, we propose a generalized Kullback-Leibler (KL) information for measuring the distance between two distribution functions where the extension to the censored case is immediate. The generalized KL information has the nonnegativity and characterization properties, and its censored version has the additional property of monotonic increase. We also extend the discussion to the discrete case and propose a generalized censored measure which is comparable to Pearson's chi-square statistic.

• 한국음향학회지
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• 제29권3호
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• pp.216-221
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• 2010

본 논문에서는 Gaussian Mixture Model (GMM)-supervector의 Mahalanobis 거리측정 방법 기반의 Support Vector Machine (SVM) 커널을 이용한 새로운 화자인증 방법을 제안한다. 제안된 GMM-supervector SVM 커널방식은 GMM 방식과 SVM 방식을 결합한 방식으로서, GMM 파라미터에 의해 형성된 화자 및 비 화자 GMM-supervectors의 화자인증 임계값을 Mahalanobis 거리측정 방법기반의 SVM 커널에 적용함으로써 화자인증 정확도를 높인다. 제안한 방식의 성능 측정을 위해 20명의 화자를 대상으로 문장독립형 화자인증 실험을 수행하여 기존에 사용되고 있는 GMM, SVM, Kullback-Leibler (KL) divergence 거리측정 방법 기반의 GMM-supervector SVM 커널, Bhattacharyya 거리측정 방법기반의 GMM-supervector SVM 커널 방식을 통한 화자인증 결과들과 비교하였다.

• 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.

• 중소기업융합학회논문지
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• 제6권4호
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• pp.71-77
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• 2016

본 논문에서는 기존의 BIC(Bayesian Information Criterion) 기반 화자변화의 성능 향상을 위하여 GMM-UBM(Gaussian Mixture Model-Universal Background Model) 기반 KL(Kullback Leibler) 거리를 활용한 화자변화 검증을 제안하였다. 정보량의 차이에 민감한 기존의 BIC 기반 화자변화검출 알고리즘을 상대적으로 정보량 차이에 견인한 KL 거리 알고리즘으로 검증하였고, 정보량의 비대칭을 보상하기 위해서 GMM-UBM을 활용하였다. 기존의 BIC 기반 화자변화 검출은 1단계로 비유사도 d가 양수인 구간의 국소 최댓값인 지점을 화자변화 후보지점으로 검출하였고, 2단계로 검출된 화자변화 후보지점 중 ${\Delta}BIC$가 양수인 지점을 화자변화지점으로 결정하였다. 본 논문에서는 BIC 기반 화자변화 검출에 의해 결정된 화자변화지점에 대하여 GMM-UBM 기반 KL 거리 D가 문턱치(threshold)보다 높은 지점을 최종 화자변화 지점으로 검증하였다. 실험결과, MDR(Missed Detection Rate)이 0인 조건에서 문턱치 0.028일 때 FAR(False Alarm Rate) 60.4%로 성능이 향상되었다.