• Title/Summary/Keyword: Under-representation

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A New Integral Representation of the Coverage Probability of a Random Convex Hull

  • Son, Won;Ng, Chi Tim;Lim, Johan
    • Communications for Statistical Applications and Methods
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    • v.22 no.1
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    • pp.69-80
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    • 2015
  • In this paper, the probability that a given point is covered by a random convex hull generated by independent and identically-distributed random points in a plane is studied. It is shown that such probability can be expressed in terms of an integral that can be approximated numerically by function-evaluations over the grid-points in a 2-dimensional space. The new integral representation allows such probability be computed efficiently. The computational burdens under the proposed integral representation and those in the existing literature are compared. The proposed method is illustrated through numerical examples where the random points are drawn from (i) uniform distribution over a square and (ii) bivariate normal distribution over the two-dimensional Euclidean space. The applications of the proposed method in statistics are are discussed.

Systematic Determination of Number of Clusters Based on Input Representation Coverage (클러스터 분석을 위한 IRC기반 클러스터 개수 자동 결정 방법)

  • 신미영
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.6
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    • pp.39-46
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    • 2004
  • One of the significant issues in cluster analysis is to identify a proper number of clusters hidden under given data. In this paper we propose a novel approach to systematically determine the number of clusters based on Input Representation Coverage (IRC), which is newly defined as a quantified value of how well original input data in Gaussian feature space can be captured with a certain number of clusters. Furthermore, its usability and applicability is also investigated via experiments with synthetic data. Our experiment results show that the proposed approach is quite useful in approximately finding the real number of clusters implicitly contained in the data.

Multiscale Spatial Position Coding under Locality Constraint for Action Recognition

  • Yang, Jiang-feng;Ma, Zheng;Xie, Mei
    • Journal of Electrical Engineering and Technology
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    • v.10 no.4
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    • pp.1851-1863
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    • 2015
  • – In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.

A design of discrete-time integral controllers under skewed sampling

  • Ishihara, Tadashi;Guo, Hai-Jiao;Taketa-Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10b
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    • pp.428-433
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    • 1993
  • First, we propose a transparent and efficient design of discrete-time integral controllers accounting sampling skew. Based on the proposed controller, we derive a state-space representation of doubly coprime factorization including integral action. The representation is then used to obtain a convenient state-space parametrization of discrete-time two-degree-of-freedom integral controllers acconting sampling skew.

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A Design of Pattern Recognition Algorithm as a Collection of Hypercubic Regions (Hypercube 영역의 집합으로 표현된 패턴인식 알고리즘의 설계)

  • Baek Sop Kim
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.29B no.7
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    • pp.23-29
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    • 1992
  • In this paper, a method of representing the pattern classifier as a collection of hypercubic regions is proposed. This representation has following advantages over the conventional ones : 1) a simple form of human knowledge can be used in designing the classifier, 2) the form of the classifier is suit for the rule-based system, and 3) this can reduce the classification time. A method of synthesis of the classifier under this representation is also proposed and the experimental result shows that the proposed method is faster than the well-known nearest neighbor classifier.

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Strong Representations for LAD Estimators in AR(1) Models

  • Kang, Hee-Jeong;Shin, Key-Il
    • Journal of the Korean Statistical Society
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    • v.27 no.3
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    • pp.349-358
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    • 1998
  • Consider the AR(1) model $X_{t}$=$\beta$ $X_{t-1}$+$\varepsilon$$_{t}$ where $\beta$ < 1 is an unknown parameter to be estimated and {$\varepsilon$$_{t}$} denotes the independent and identically distributed error terms with unknown common distribution function F. In this paper, a strong representation for the least absolute deviation (LAD) estimate of $\beta$ in AR(1) models is obtained under some mild conditions on F. on F.F.

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Solving Robust EOQ Model Using Genetic Algorithm

  • Lim, Sung-Mook
    • Management Science and Financial Engineering
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    • v.13 no.1
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    • pp.35-53
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    • 2007
  • We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

Simple Estimation in Proportional Odds Model under Censoring

  • Kim, Ju-Sung;Seo, Min-Ja;Won, Dong-Yu
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.889-898
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    • 2005
  • In this paper we propose a new estimator of relative odds ratio in the two-sample case of proportional odds model under censorship. Also, we show that the estimator consistent and asymptotically normal by using martingale-representation. The efficiency of the proposed is assessed through a simulation study.

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Development of Replacement Models under Minimal Repair with Wavelet Failure Rate Functions (웨이브릿 고장률 함수를 갖는 최소수리 교체모형 개발)

  • 최성운
    • Journal of the Korea Safety Management & Science
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    • v.3 no.4
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    • pp.91-101
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    • 2001
  • This paper is to develop replacement models under minimal repair with exponential polynomial wavelet failure rate function. Wavelets have good time-frequency localization, fast algorithms and parsimonious representation. Also this study is presented along with numerical examples using sensitivity analysis for exponential polynomial trigonometric failure rate function.

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