• 제목/요약/키워드: Optimal Hyperplanes

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불완전 데이터의 패턴 분석을 위한 $_{MI}$SVMs (A New Support Vector Machines for Classifying Uncertain Data)

  • Kiyoung, Lee;Dae-Won, Kim;Doheon, Lee;Kwang H., Lee
    • 한국정보과학회:학술대회논문집
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    • 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (2)
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    • pp.703-705
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    • 2004
  • Conventional support vector machines (SVMs) find optimal hyperplanes that have maximal margins by treating all data equivalently. In the real world, however, the data within a data set may differ in degree of uncertainty or importance due to noise, inaccuracies or missing values in the data. Hence, if all data are treated as equivalent, without considering such differences, the optimal hyperplanes identified are likely to be less optimal. In this paper, to more accurately identify the optimal hyperplane in a given uncertain data set, we propose a membership-induced distance from a hyperplane using membership values, and formulate three kinds of membership-induced SVMs.

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불확실성을 갖는 비선형 가변구조시스템의 슬라이딩 초평면 설계 (Design of Sliding Hyperplanes in Nonlinear Variable Structure Systems with Uncertainties)

  • 박동원;최승복;김재문
    • 대한기계학회논문집
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    • 제18권8호
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    • pp.1985-1996
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    • 1994
  • A new design method of sliding hyperplanes is proposed in the synthesis of a variable structure controller for robust tracking of general nonlinear multi-input-output(MIMO) uncertain systems of relative degree higher than two. Input/ output(I/O) linearzation is firstly undertaken by employing the concept of relative degree and minimum phase followed by the construction of sliding mode controllers. Sliding hyperplanes are then derived from the inherent properties of companion matrix and ideal sliding mode characterized in I/O linearized system. Subsequently, the gradient magnitudes of the sling hyperplanes are determined in an optimal manner by considering a quadratic performance index to be evaluated at two phases; a reaching phase and a sliding phase. The proposed design methodology is relatively straightforward and systematic compared with conventional strategies such as geometric approach or pole assignment technique. A nonlinear governor and exciter control problem for a power system is adopted herein in order to demonstrate the design efficiency and also favorable and robust control performances.

Global Optimization of Clusters in Gene Expression Data of DNA Microarrays by Deterministic Annealing

  • Lee, Kwon Moo;Chung, Tae Su;Kim, Ju Han
    • Genomics & Informatics
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    • 제1권1호
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    • pp.20-24
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    • 2003
  • The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non­zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.

Membership Function-based Classification Algorithms for Stability improvements of BCI Systems

  • Yeom, Hong-Gi;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제10권1호
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    • pp.59-64
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    • 2010
  • To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate nonlinear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.

다중 선택 배낭 제약식 하에서의 오목 함수 최소화 문제 (An Concave Minimization Problem under the Muti-selection Knapsack Constraint)

  • 오세호
    • 한국융합학회논문지
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    • 제10권11호
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    • pp.71-77
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    • 2019
  • 본 연구에서는 다중 선택 배낭 모형의 최적해를 찾는 해법을 제시하고자 한다. 다중 선택은 동일한 집단에 소속된 구성원들이 동시에 선택되거나 동시에 배제되는 상황에서 관찰된다. 각 집단 간 관련성의 측정치인 오목 함수가 의사결정기준으로 설정되었다. 다중 선택은 비선형 제약식으로 모형화 되는데 일반 배낭 제약식으로 변환될 수 있다. 따라서 최적 해법 개발을 위해 오목함수 최소화 문제와 배낭 문제의 일반적인 해법들에서 채택하고 있는 분지 한계 접근법을 이용하였다. 단체상에서 오목함수를 가장 근접하게 하한추정하는 함수가 1차식이라는 사실이 한계 전략의 이론적 토대가 된다. 또한 하위 단계에서도 1차식 목적함수가 유일하게 결정되도록, 후보 단체를 두 개의 초평면에 투사시킴으로써 1차원 낮은 두 개의 하위 단체로 분할하는 방법이 분지 전략의 핵심이다. 앞으로 본 연구의 결과는 다양한 형태의 배낭 제약식 하에서의 오목 함수 최소화 문제의 해법을 개발하는데 응용될 수 있을 것이다.