• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.959-965
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• 2010

For multiclassification, it is often the case that some variables are not important while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables for multiclassification. This algorithm is base on multiclass least squares support vector machine (LS-SVM), which uses results of multiclass LS-SVM using one-vs-all method. Experimental results are then presented which indicate the performance of the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.999-1012
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• 2005

We compare two popular algorithms in current machine learning and statistical learning areas, boosting method represented by AdaBoost and kernel based SVM (Support Vector Machine) using 13 real data sets. This comparative study shows that boosting method has smaller prediction error in data with heavy noise, whereas SVM has smaller prediction error in the data with little noise.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.315-315
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• 2000

In this paper, we perform the time series prediction using the SVM(Support Vector Machine). We make use of two different loss functions and two different kernel functions; i) Quadratic and $\varepsilon$-insensitive loss function are used; ii) GRBF(Gaussian Radial Basis Function) and ERBF(Exponential Radial Basis Function) are used. Mackey-Glass time series are used for prediction. For both cases, we compare the results by the SVM to those by ANN(Artificial Neural Network) and show the better performance by SVM than that by ANN.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.517-524
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• 2015

Unlabeled examples are easier and less expensive to be obtained than labeled examples. In this paper semisupervised approach is used to utilize such examples in an effort to enhance the predictive performance of nonlinear quantile regression problems. We propose a semisupervised quantile regression method named semisupervised support vector quantile regression, which is based on support vector machine. A generalized approximate cross validation method is used to choose the hyper-parameters that affect the performance of estimator. The experimental results confirm the successful performance of the proposed S2SVQR.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.843-848
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• 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.

• Korean Journal of Cognitive Science
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• v.19 no.1
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• pp.1-15
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• 2008

Analyzing of dependency relation among clauses is one of the most critical parts in parsing Korean sentences because it generates severe ambiguities. To get successful results of analyzing dependency relation, this task has been the target of various machine learning methods including SVM. Especially, kernel methods are usually used to analyze dependency relation and it is reported that they show high performance. This paper proposes an expression and a composit kernel for dependency analysis of Korean clauses. The proposed expression adopts a composite kernel to obtain the similarity among clauses. The composite kernel consists of a parse tree kernel and a liner kernel. A parse tree kernel is used for treating structure information and a liner kernel is applied for using lexical information. the proposed expression is defined as three types. One is a expression of layers in clause, another is relation expression between clause and the other is an expression of inner clause. The experiment is processed by two steps that first is a relation expression between clauses and the second is a expression of inner clauses. The experimental results show that the proposed expression achieves 83.31% of accuracy.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.455-464
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• 2014

Unlabeled examples are easier and less expensive to obtain than labeled examples. Semisupervised approaches are used to utilize such examples in an eort to boost the predictive performance. This paper proposes a novel semisupervised classication method named transductive least squares support vector machine (TLS-SVM), which is based on the least squares support vector machine. The proposed method utilizes the dierence convex algorithm to derive nonconvex minimization solutions for the TLS-SVM. A generalized cross validation method is also developed to choose the hyperparameters that aect the performance of the TLS-SVM. The experimental results conrm the successful performance of the proposed TLS-SVM.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.233-240
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• 2003

Recently, the world are preparing for new revolution, called as If (Information Technology), NT (Nano-Technology), and BT (Bio-Technology). NT can be applied to various fields such as semiconductor-micro technology. Ultra precision processing is required for NT in the field of mechanical engineering. Recently, together with radical advancement of electronic and photonics industry, necessity of ultra precision processing is on the increase for the manufacture of various kernel parts. Therefore, in this paper, stability of ultra precision cutting unit is investigated, this unit is the kernel unit in ultra precision processing machine. According to alteration of shape and material about hinge, stability investigation is performed. In this paper, hinge shapes of micro stage in UPCU(Ultra Precision Cutting Unit) are designed as two types, where, hinge shapes are composed of round and rectangularity. Elasticity and strength are analyzed about micro stage, according to hinge shapes, by FE analysis. Micro stage in ultra precision processing machine has to keep hinge shape under cutting condition with 3-component force (cutting component, axial component, radial component) and to reduce modification against cutting force. Then we investigated its elasticity and its strength against these conditions. Material of micro stage is generally used to duralumin with small thermal deformation. But, stability of micro stage is investigated, according to elasticity and strength due to various materials, by FE analysis. Where, Used materials are composed of aluminum of low strength and cooper of medium strength and spring steel of high strength. Through this stability investigation, trial and error is reduced in design and manufacture, at the same time, we are accumulated foundation data for unit control.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.249-251
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• 2022

In this paper, we present machine learning to control drone flight using EEG signals. We defined takeoff, forward, backward, left movement and right movement as control targets and measured EEG signals from the frontal lobe for controlling using Fp1. Fp2 Fp2 two-channel dry electrode (NeuroNicle FX2) measuring at 250Hz sampling rate. And the collected data were filtered at 6~20Hz cutoff frequency. We measured the motion image of the action associated with each control target open for 5.19 seconds. Using Matlab's classification learner for the measured EEG signal, the triple layer neural network, logistic regression kernel, nonlinear polynomial Support Vector Machine(SVM) learning was performed, logistic regression kernel was confirmed as the highest accuracy for takeoff and forward, backward, left movement and right movement of the drone in learning by class True Positive Rate(TPR).

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.