• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.21-28
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• 2015

Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale in each feature dimension and error rate in classification. Most of the existing normalization methods, however, do not use the class labels of data points and, as a result, do not guarantee the optimality of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature normalization to reduce classification error more. Training sample selection is a common technique for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method.

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.19-26
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• 2013

Pseudo sample neural network (PSNN) is a variant of traditional neural network using pseudo samples to mitigate the local-optima-convergence problem when the size of training samples is small. PSNN can take advantage of the smoothed solution space through the use of pseudo samples. PSNN has a focus on the quantity problem in training, whereas, methods stressing the quality of training samples is presented in this paper to improve further the performance of PSNN. It is evident that typical samples and highly correlated features help in training. In this paper, therefore, kernel density estimation is used to select typical samples and correlation factor is introduced to select features, which can improve the performance of PSNN. Debris flow data set is used to demonstrate the usefulness of the proposed methods.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1684-1690
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• 2004

This paper presents a method for classifier selection that uses distribution information of the training samples in a small region surrounding a sample. The conventional DCS-LA(Dynamic Classifier Selection - Local Accuracy) selects a classifier dynamically by comparing the local accuracy of each classifier at the test time, which inevitably requires long classification time. On the other hand, in the proposed approach, the best classifier in a local region is stored in the FSA(Feature Space Attribute) table during the training time, and the test is done by just referring to the table. Therefore, this approach enables fast classification because classification is not needed during test. Two feature space attributes are used entropy and density of k training samples around each sample. Each sample in the feature space is mapped into a point in the attribute space made by two attributes. The attribute space is divided into regular rectangular cells in which the local accuracy of each classifier is appended. The cells with associated local accuracy comprise the FSA table. During test, when a test sample is applied, the cell to which the test sample belongs is determined first by calculating the two attributes, and then, the most accurate classifier is chosen from the FSA table. To show the effectiveness of the proposed algorithm, it is compared with the conventional DCS -LA using the Elena database. The experiments show that the accuracy of the proposed algorithm is almost same as DCS-LA, but the classification time is about four times faster than that.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.206-212
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• 2024

This study presents five selection rules for training algorithms to extract audio sources from noise. The five rules are Dynamics, Roots, Tonal Balance, Tonal-Noisy Balance, and Stereo Width, and the suitability of each rule for sound extraction was determined by spectrogram analysis using various types of sample sources, such as environmental sounds, musical instruments, human voice, as well as white, brown, and pink noise with sine waves. The training area of the algorithm includes both melody and beat, and with these rules, the algorithm is able to analyze which specific audio sources are contained in the given noise and extract them. The results of this study are expected to improve the accuracy of the algorithm in audio source extraction and enable automated sound clip selection, which will provide a new methodology for sound processing and audio source generation using noise.

• Asian Journal of Business Environment
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• v.5 no.3
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• pp.23-28
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• 2015

Purpose - The aim of this study is to evaluate the effectiveness of a corporate training program. The case study of HCL Technologies was used to investigate how training programs improve the performance of employees on the job, as well as to identify unnecessary aspects of the training for the purpose of eliminating these from future training programs. Research design, data, and methodology - An exploratory research design was used to conduct the study. The research sample size included 50 HCL employees. The sampling technique for the data collection was convenience sampling. Results - Training is a crucial process in an organization and thus needs to be well designed. Specifically, the training programs should provide adequate knowledge to all employees, ensure correct methods are used for the selection of trainees, and avoid any perception of biasness. Conclusions - Employees were not fully satisfied by the separation of the training program into two parts, on the job and off the job training, but if sufficient data is provided to employees in advance, this could help them during the training process.

• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.73-92
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• 1999

In Bayesian model selection or testing problems, one cannot utilize standard or default noninformative priors, since these priors are typically improper and are defined only up to arbitrary constants. The resulting Bayes factors are not well defined. A recently proposed model selection criterion, the intrinsic Bayes factor overcomes such problems by using a part of the sample as a training sample to get a proper posterior and then use the posterior as the prior for the remaining observations to compute the Bayes factor. Surprisingly, such Bayes factor can also be computed directly from the full sample by some proper priors, namely intrinsic priors. The present paper explains how to derive intrinsic priors for simple tree ordered exponential means. Some numerical results are also provided to support theoretical results and compare with classical methods.

• Korea Journal of Hospital Management
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• v.10 no.3
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• pp.1-24
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• 2005

This study purports to investigate the determinants of individual personnel rating of the employees at a university hospital in Seoul, Korea. The sample used in this study consisted of 63 nurses, 41 para-medical staff (Clinical Pathologist, and Radiologist), and 67 administrative staff. Independent variables of the study included the achievement level of the selection test (English, major subject, and interview), post-entrance development factors (education and training, career development, supervisory support, co-worker support, and organizational support), and demographic characteristics. Data for the achievement level of the entrance exam and years for the first promotion were collected from the administrative records of the study hospital, while data for the post-entrance development factors were collected from the survey with self-administered questionnaires using 5-point Likert Scale during June 10-25, 2003. Collected data were analyzed using hierarchical multiple regression. The results of the study showed that achievement level of the interview and English exam at the selection test, education and training, organizational support, and supervisory support while working at the hospital, and length of duration (below 8 years) and educational background (4-year college graduates) among demographic variables had significant positive effects on the personnel rating. The results of the study imply that hospital administrators should make an effort to improve the validity of the selection test, and to motivate the employees to receive more education and training.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.325-337
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• 1993

This paper propose two test statistics which enable us to proceed the variable selection in Fisher's linear discriminant function for the case of heterogeneous discrimination with equal training sample size. Simultaneous confidence intervals associated with the test are also given. These are exact and approximate results. The latter is based upon an approximation of a linear sum of Wishart distributions with unequal scale matrices. Using simulated sampling experiments, powers of the two tests have been tabulated, and power comparisons have been made between them.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.435-449
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• 1998

In this article we have introduced a Bayes criterion for the variable selection in multiple discriminant analysis (MDA). The criterion is a default Bayes factor for the comparision of homo/heteroscadasticity of the multivariate normal means. The default Bayes factor is obtained from a development of the imaginary training sample method introduced by Spiegelhalter and Smith (1982). Based an the criterion, we also provided a test for additional discrimination in MDA. The advantage of the criterion is that it is not only applicable for the optimal subset selection method but for the stepwise method. More over, the criterion can be reduced to that for two-group discriminant analysis. Thus the criterion can be regarded as an unified alternative to variable selection criteria suggested by various sampling theory approaches. To illustrate the performance of the criterion, a numerical study has bean done via Monte Carlo experiment.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.28 no.1
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• pp.71-80
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• 2022

Background: This study investigated the effect of robot-assisted arm training on muscle activity of arm and weight bearing in stroke patients. Methods: The study subjects were selected 20 stroke patients who met the selection criteria. 10 people in the robot-assisted arm training group and 10 people in the task-oriented arm training group were randomly assigned. The experimental group performed robot-assisted arm training, and the control group performed task-oriented arm training for 6 weeks, 5 days a week, 30 minutes a day. The measurement tools included surface electromyography and smart insole system. Data were analyzed using independent sample t-test and the paired sample t-test. Results: Comparing the muscle activity of arm within the group, the experimental group and the control group showed significant differences in muscle activity in the biceps brachii, triceps brachii, anterior deltoid, upper trapezius, middle trapezius, and lower trapezius. Comparing the muscle activity of arms between the groups, the experimental group showed significant difference in all muscle activity of arm compared to the control group. Comparing the weight bearing within the groups, the experimental group showed significant difference in the affected side and non-affected side weight bearings and there were significant differences in anterior and posterior weight bearing. The control group showed significant difference only in the non-affected side weight bearing. Comparing the weight bearings between groups, the experimental group showed significant difference in the affected side and non-affected side weight bearings compared to the control group. Conclusion: This study confirmed that robot-assisted arm training applied to stroke patients for 6 weeks significantly improved muscle activity of arm and weight bearing. Based on these results, it is considered that robot-assisted arm training can be a useful treatment in clinical practice to improve the kinematic variables in chronic stroke patients.