• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.55-65
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• 2000

This paper presents a linear programming based algorithm for pattern classification. Pattern classification is being considered to be critical in the area of artificial intelligence and business applications. Previous methods employing linear programming have been aimed at two-group discrimination with one or more linear discriminant functions. Therefore, there are some limitations in applying available linear programming formulations directly to general multi-class classification problems. The algorithm proposed in this manuscript is based on quadratic or polynomial discriminant functions, which allow more flexibility in covering the class regions in the N-dimensional space. The proposed algorithm is compared with other competitive methods of pattern classification in experimental results and is shown to be competitive enough for a general purpose classifier.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.74-81
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• 2006

In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear discriminant functions. Toward the end, we develop a new $l_1$-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming (MILP) model. Given a finite number of discriminant functions as an input, the proposed model considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear decision surface that best separates the data on hand. Hence, exploiting powerful MILP solvers, the model efficiently analyzes any given data set for its piecewise nonlinear separability. The classification of four sets of artificial data demonstrates the aforementioned strength of the proposed model. Classification results on five machine learning benchmark databases prove that the data separation via the proposed MILP model is an effective supervised learning methodology that compares quite favorably to well-established learning methodologies.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.225-232
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• 2000

The linear and quadratic discrimination functions based on normal theory are widely used to classify an observation to one of predefined groups. But the discriminant functions are sensitive to outliers. A high breakdown procedure to estimate location and scatter of multivariate data is the minimum volume ellipsoid or MVE estimator To obtain high breakdown classifiers outliers in multivariate data are detected by using the robust Mahalanobis distance based on MVE estimators and the weighted estimators are inserted in the functions for classification. A samll-sample MOnte Carlo study shows that the high breakdown robust procedures perform better than the classical classifiers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.359-365
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• 2020

Sasang Constitution data that were classified by the QSCCII (Questionnaire for the Sasang Constitution Classification II) and artery pulse waves of Chon, Guan, and Chuck data measured using an electronic manometer, were obtained from 732 subjects who visited an oriental hospital. The pulse width, peak height, and number of peaks were extracted from the pulse waves as feature variables. Validity and reliability analyses were performed to obtain the feature variables. The feature variables with high validity and reliability were selected as the discriminant variables. The pulse wave data were divided into training and predicting samples by applying a fivefold cross-validation technique. Discriminant analysis was performed for the training sample, and discriminant functions were obtained. The discriminant functions were applied to the predicting sample and the Sasang Constitution was predicted. The accuracy of prediction was estimated by comparing the predicted Sasang Constitution and that obtained by QSCCII. The accuracy of the predicted Sasang Constitution before (after) age and sex calibration was 73.6 % (70.4 %), 68.4 % (84.2 %), and 74.2 % (67.7 %) for Taeumin, Soumin, and Soyangin, respectively, and 72.5 % (73.8 %) in total.

• Journal of Families and Better Life
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• v.31 no.5
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• pp.201-210
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• 2013

The purpose of this study was to investigate the possibility of discriminating a high level of school adjustment in low-income school-aged children using interpersonal-related variables(mother attachment, peer attachment) and self-related variables(ego-resiliency, self-control). The subjects were 335 children in fourth, fifth and sixth grades in 4 elementary schools in Daegu. Mean(SD), t-test, and stepwise discriminant analysis were used for data analysis. Base on the results of the discriminant analysis, the discriminant functions suggested that the best predictor for distinguishing between a high level of school adjustment in low-income school-aged children and a low level of school adjustment was ego-resiliency. Self-control, mother attachment and peer attachment reliably separated the groups. And using ego-resiliency, self-control, mother attachment and peer attachment as predictors, the discriminant analysis correctly classified 92.3% of the participants.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.1
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• pp.213-219
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• 2013

A class invariant is the value of a modular function that generates a ring class field of an imaginary quadratic number field such as the singular moduli of level 1. In this paper, we compute the Galois actions of a class invariant from a generalized Weber function $g_1$ over imaginary quadratic number fields with discriminant $D{\equiv}64(mod72)$.

• Journal of the Chungcheong Mathematical Society
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• v.24 no.4
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• pp.921-925
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• 2011

A class invariant is the value of a modular function that generates a ring class field of an imaginary quadratic number field such as the singular moduli of level 1. In this paper, using Shimura Reciprocity Law, we compute the Galois actions of a class invariant from a generalized Weber function $g_2$ over quadratic number fields with discriminant $D{\equiv}21$ (mod 36).

• Journal of the Chungcheong Mathematical Society
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• v.23 no.4
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• pp.853-860
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• 2010

A class invariant is the value of a modular function that generates a ring class field of an imaginary quadratic number field such as the singular moduli of level 1. In this paper, using Shimura Reciprocity Law, we compute the Galois actions of a class invariant from a generalized Weber function $g_2$ over quadratic number fields with discriminant $D{\equiv}-3$ (mod 36).

• Journal of the Korean Institute of Educational Facilities
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• v.4 no.4
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• pp.29-41
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• 1997

The objective of this paper is to construct a model to be used in deciding whether to repair or rebuild school buildings is depending on their ages and other factors. The theme of this paper is the age is the main variable but other factors such as floor, innerwall, ceiling, door, inner window of the class room, outer window of the class room, inner window of the corridor, outer window of the corridor, middle window between the classroom and the corridor, light, heater, speaker, fire protection sensor, TV monitor, and telephone status would influence the final decisions. This paper employs an experimental case study method. Using the stepwise, statistical, classification method commonly used in discriminant analysis, it evaluates 12,766 rooms of 87 different high schools in Seoul. The result of this study indicates that some critical variables influencing the final decisions are the status of TV monitor, middle window between the classroom and the corridor, light, inner window of the corridor, fire protection sensor, innerwall, speaker utensil, outer window of the class room, and door of the class room. This paper also suggests a linear discriminant function will be used for this kind of studies. Finally the paper recommends policies with respect to the variables and discriminant functions evaluated.