• Journal of the Korean Data and Information Science Society
• /
• 제21권2호
• /
• pp.363-369
• /
• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• Journal of the Korean Data and Information Science Society
• /
• 제17권3호
• /
• pp.905-912
• /
• 2006

Support vector classification(SVC) provides more complete description of the linear and nonlinear relationships between input vectors and classifiers. In this paper we propose to solve the optimization problem of SVC with a modified hinge loss function, which enables to use an iterative reweighted least squares(IRWLS) procedure. We also introduce the approximate cross validation function to select the hyperparameters which affect the performance of SVC. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Communications for Statistical Applications and Methods
• /
• 제6권3호
• /
• pp.843-848
• /
• 1999

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.

• 상하수도학회지
• /
• 제35권4호
• /
• pp.259-275
• /
• 2021

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권12호
• /
• pp.6190-6213
• /
• 2019

For small-scale vector data, restrictions on watermark scheme capacity and robustness limit the use of copyright protection. A watermarking scheme based on robust geometric features and capacity maximization strategy that simultaneously improves capacity and robustness is presented in this paper. The distance ratio and angle of adjacent vertices are chosen as the watermark domain due to their resistance to vertex and geometric attacks. Regarding watermark embedding and extraction, a capacity-improved strategy based on quantization index modulation, which divides more intervals to carry sufficient watermark bits, is proposed. By considering the error tolerance of the vector map and the numerical accuracy, the optimization of the capacity-improved strategy is studied to maximize the embedded watermark bits for each vertex. The experimental results demonstrated that the map distortion caused by watermarks is small and much lower than the map tolerance. Additionally, the proposed scheme can embed a copyright image of 1024 bits into vector data of 150 vertices, which reaches capacity at approximately 14 bits/vertex, and shows prominent robustness against vertex and geometric attacks for small-scale vector data.

• 대한기계학회논문집A
• /
• 제36권7호
• /
• pp.805-811
• /
• 2012

신뢰성 기반 최적설계의 효과적인 수행을 위하여 개발된 단일루프 단일벡터 방법은 신뢰성 해석의 계산과정을 제거함으로써 최적설계 시 발생하는 과도한 계산비용을 줄일 수 있다. 하지만 성능함수의 오목한 정도가 심할 경우, 수렴을 하지 못하고 발산하는 경향을 보인다. 때문에 일반적인 단일루프 단일벡터 방법은 낮은 수렴성과 부정확성 문제를 내포하고 있다. 본 연구에서는 공액경사도법을 이용한 단일루프 단일벡터 방법을 제안한다. 공액경사도법은 이전 반복과정의 최대가능손상점에서 계산된 방향 벡터들을 이용하여 현재 설계점에서의 최대가능손상점을 산출하기 위한 새로운 방향벡터를 구하고 이 방향벡터를 이용하여 현재점에서의 최적화를 수행한다. 이를 다양한 수학예제에 적용하고 다른 방법들과 수치적 성능 비교를 통해 제안한 방법의 유용성을 검증한다. 공액경사도법을 이용한 단일루프 단일벡터 방법은 성능함수 특성에 크게 영향을 받지 않으며 수렴성을 크게 향상시킬 수 있다.

• ETRI Journal
• /
• 제32권1호
• /
• pp.120-128
• /
• 2010

In this paper, we describe a modified fixed-threshold sequential minimal optimization (FSMO) for 1-slack structural support vector machine (SVM) problems. Because the modified FSMO uses the fact that the formulation of 1-slack structural SVMs has no bias, it breaks down the quadratic programming (QP) problems of 1-slack structural SVMs into a series of smallest QP problems, each involving only one variable. For various test sets, the modified FSMO is as accurate as existing structural SVM implementations (n-slack and 1-slack SVM-struct) but is faster on large data sets.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권10호
• /
• pp.5013-5034
• /
• 2019

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.

• International Journal of Automotive Technology
• /
• 제6권3호
• /
• pp.235-242
• /
• 2005

This study introduces the Reliability-Based Design Optimization (RBDO) to enhance the kinematic and compliance (K & C) characteristics of automotive suspension system. In previous studies, the deterministic optimization has been performed to enhance the K & C characteristics. Unfortunately, uncertainties in the real world have not been considered in the deterministic optimization. In the design of suspension system, design variables with the uncertainties, such as the bushing stiffness, have a great influence on the variation of the suspension performances. There is a need to quantify these uncertainties and to apply the RBDO to obtain the design, satisfying the target reliability level. In this research, design variables including uncertainties are dealt as random variables and reliability of the suspension performances, which are related the K & C characteristics, are quantified and the RBDO is performed. The RBD-optimum is compared with the deterministic optimum to verify the enhancement in reliability. Thus, the reliability of the suspension performances is estimated and the RBD-optimum, satisfying the target reliability level, is determined.

• 한국안전학회지
• /
• 제12권3호
• /
• pp.120-131
• /
• 1997

This paper presents an approximate reanalysis methods of structures based on substructuring for an effective optimization of large-scale structural systems. In most optimal design procedures the analysis of the structure must be repeated many times. In particular, one of the main obstacles in the optimization of structural systems are involved high computational cost and expended long time in the optimization of large-scale structures. The purpose of this paper is to evaluate efficiently the structural behavior of new designs using information from previous ones, without solving basic equations for successive modification in the optimal design. The proposed reanalysis procedure is combined Taylor series expansions which is a local approximation and reduced basis method which is a global approximation based on substructuring. This technique is to choose each of the terms of Taylor series expansions as the basis vector of reduced basis method in substructuring system which is one of the most effective analysis of large -scale structures. Several numerical examples illustrate the effectiveness of the solution process.