• Journal of the Korean Statistical Society
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• 제27권2호
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• pp.153-163
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• 1998

We investigate density estimation problem in the L$^{\infty}$ norm and show that the iii optimal minimax rates are achieved for smooth classes of weakly dependent stationary sequences. Our results are then applied to give uniform convergence rates for various problems including the Gibbs sampler.

• 한국정보처리학회논문지
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• 제7권8호
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• pp.2520-2527
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• 2000

전력 평가를 위한 전이 밀도 전파에 대한 개선된 방법을 제안한다. 제로 지연 모델을 위한 전력 평가는 전력 소모의 하한 경계 값에 대한 적절한 기준이다. 전력 예측을 위한 하한 값으로 제로 지연 모델을 사용한 전이 전파 방법에 대한 연구가 있지만 전이 밀도 전파 과정 중 잉여 요소들을 제거하지 못하였다. 본 연구에서는 전이 전파 밀도 동작을 분석함에 의하여 잉여 요소들을 제거할 수 있는 향상된 신호 전이 밀도 예측 기법을 제안한다. 실험에 의하면 제안된 방법이 기존의 방법에 비하여 상대적으로 양호한 예측 정확도를 나타낸다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• Journal of the Korean Wood Science and Technology
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• 제23권3호
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• pp.58-65
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• 1995

The possibilities of the estimation of the moisture content(MC) for sitka-spruce (Picea sitchensis Carr.) by measuring density have been investigated. The method is based on the relationships between the wood density and moisture content of wood expressed by Equations (8)~(9). The purpose of this study is examining the estimation of the moisture content of wood by density and the variation of moisture content with annual ring width of wood. The following conclusions were obtained; 1. This method is very convenience because of the average moisture content of wood can be obtained by a simple estimation. This estimation can be made from the easy measurement of the weight and volume of wood. 2. Coefficient of determination between the experimental MCs and theoretical MCs which is calculated by the oven-dry densities of each specimens and Equations (8), (9) is 0.98. This Correlation is very remarkable. Therefore the model Equations on the estimation of moisture content by wood density was available. 3. Relationship between experimental MCs and theoretical MCs which is estimated by average oven-dry density of total specimens showed positive correlation(Fig.2). But from the Fig.4. we can concluded that the number of specimens is two groups. This phenomenon is considered that the variation of MC by the annual ring width from the specimens' observations. Consequently, the MCs of wood by density, is likely to be successful method. can be estimate using by the average oven-dry densities divided with the annual ring widths of wood.

• 한국수학사학회지
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• 제16권3호
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• pp.95-100
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• 2003

The recent surge of interest in the more technical aspects of nonparametric density estimation and nonparametric regression estimation has brought the subject into public view. In this paper, we investigate the general concept of the nonparametric density estimation, the nonparametric regression estimation and its performance criteria.

• 한국지구과학회지
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• 제26권2호
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• pp.114-128
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• 2005

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

• Journal of the Korean Data and Information Science Society
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• 제3권1호
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• pp.17-24
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• 1992

The problem of estimating symmetric probability density with high kurtosis is considered. Such densities are often estimated poorly by a global bandwidth kernel estimation since good estimation of the peak of the distribution leads to unsatisfactory estimation of the tails and vice versa. In this paper, we propose a transformation technique before using a global bandwidth kernel estimator. Performance of density estimator based on proposed transformation is investigated through simulation study. It is observed that our method offers a substantial improvement for the densities with high kurtosis. However, its performance is a little worse than that of ordinary kernel estimator in the situation where the kurtosis is not high.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.76-78
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• 2014

나이브 베이스 학습은 유명하면서도, 빠르면서도 효과적인 지도 학습 방법으로, 다소 잡음을 가진 라벨이 있는 데이터집합을 다루는 데 좋은 성능을 보인다. 그러나, 나이브 베이스의 조건적 독립성 가정은 실세계 데이터를 다루는 데 필요한 특성에 다소 제약사항을 가지게 한다. 지금까지 연구자들이 이 조건적 독립성 가정을 완화시키는 방법들을 제안해 왔다. 이러한 방법들은 어트리뷰트 가중치, 커널 밀도 측정 등이 있다. 본 논문에서, 우리는 커널 밀도 측정과 어트리뷰트 가증치를 이용하여 나이브 베이스의 학습 효과를 개선하기 위한 NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) 이라는 새로운 접근 방법을 제안한다.

• Journal of the Korean Statistical Society
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• 제30권3호
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• pp.453-465
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• 2001

It is often the case that one wants to estimate parameters of the distribution which follows certain parametric model, while the dta are contaminated. it is well known that the maximum likelihood estimators are not robust to contamination. Basuet al.(1998) proposed a robust method called the minimum density power divergence estimation. In this paper, we investigate data-driven selection of the tuning parameter $\alpha$ in the minimum density power divergence estimation. A criterion is proposed and its performance is studied through the simulation. The simulation includes three cases of estimation problem.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1159-1165
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• 2005

Basu et al. (1998) proposed a new density-based estimator, called the minimum density power divergence estimator (MDPDE), which avoid the use of nonparametric density estimation and associated complication such as bandwidth selection. Woodward et al. (1995) examined the minimum Hellinger distance estimator (MHDE), proposed by Beran (1977), in the case of estimation of the mixture proportion in the mixture of two normals. In this article, we introduce the MDPDE for a mixture proportion, and show that both the MDPDE and the MHDE have the same asymptotic distribution at a model. Simulation study identifies some cases where the MHDE is consistently better than the MDPDE in terms of bias.