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46 results on '"Shin, Kyung-shik"'

6. An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case

Author

Kim, Sun, Shin, Kyung-shik, Park, Kyungdo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Lipo, editor, Chen, Ke, editor, and Ong, Yew Soon, editor

Published
2005
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7. Time Delay Neural Networks and Genetic Algorithms for Detecting Temporal Patterns in Stock Markets

Author

Kim, Hyun-jung, Shin, Kyung-shik, Park, Kyungdo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Lipo, editor, Chen, Ke, editor, and Ong, Yew Soon, editor

Published
2005
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8. PPSS: CBR System for ERP Project Pre-planning

Author

Kwon, Suhn Beom, Shin, Kyung-shik, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and Kim, Tag Gon, editor

Published
2005
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9. Support Vector Machines Approach to Pattern Detection in Bankruptcy Prediction and Its Contingency

Author

Shin, Kyung-shik, Lee, Kyoung Jun, Kim, Hyun-jung, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pal, Nikhil Ranjan, editor, Kasabov, Nik, editor, Mudi, Rajani K., editor, Pal, Srimanta, editor, and Parui, Swapan Kumar, editor

Published
2004
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10. Bankruptcy Prediction Modeling Using Multiple Neural Network Models

Author

Shin, Kyung-shik, Lee, Kyoung Jun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Negoita, Mircea Gh., editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2004
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11. Neuro-Genetic Approach for Bankruptcy Prediction Modeling

Author

Shin, Kyung-shik, Lee, Kyoung Jun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Negoita, Mircea Gh., editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2004
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12. A GA-Based Fuzzy Decision Tree Approach for Corporate Bond Rating

Author

Shin, Kyung-shik, Kim, Hyun-jung, Kwon, Suhn-beom, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Zhang, Chengqi, editor, W. Guesgen, Hans, editor, and Yeap, Wai-Kiang, editor

Published
2004
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29. A performance comparison between the two language versions of the Affective Go/No-Go test: A randomised crossover study

Author

Lyoo, Younghyun C., primary, Park, Shinwon, additional, Ju, Sejin, additional, Shin, Kyung-Shik, additional, Hwang, Jaeuk, additional, Yoon, Sujung, additional, Kim, Jungyoon, additional, Cho, Han Byul, additional, Kim, Young Hoon, additional, Kim, Jihyun H., additional, Jeong, Hyeonseok S., additional, and Kim, Jieun E., additional

Published
2017
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30. A performance comparison between the two language versions of the Affective Go/No‐Go test: A randomised crossover study.

Author

Lyoo, Younghyun C., Park, Shinwon, Ju, Sejin, Shin, Kyung‐Shik, Hwang, Jaeuk, Yoon, Sujung, Kim, Jungyoon, Cho, Han Byul, Kim, Young Hoon, Kim, Jihyun H., Jeong, Hyeonseok S., and Kim, Jieun E.

Subjects
LANGUAGE & emotions, PSYCHOMETRICS, KOREANS, ANALYSIS of variance, EMOTIONS, AFFECT (Psychology), ATTITUDE (Psychology)
Abstract

Abstract: The purpose of this randomised crossover study is to validate the Korean version of the Affective Go/No‐go (AGN) test. The Korean words for the AGN test were selected after careful evaluation of emotional valences, word length and frequency. Fifteen Korean advanced learners of English were administered both Korean and English versions, yielding 30 data points. The performance of both language versions was compared for each of the AGN test parameters (response latency, commission error and omission error) using two‐way analysis of variance (ANOVA). Intraclass correlation coefficients (ICCs) were estimated to evaluate associations between the two versions. The ICCs were high for response latencies of all valences and commission errors of positive and neutral words, but not for that of negative words and omission errors of all valences. A similar pattern of test results, as revealed by the high ICCs and non‐significant interaction effects between language and word valence, suggests that the psychometric properties of the AGN test may be comparable over different language versions. [ABSTRACT FROM AUTHOR]

Published
2018
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45. An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case.

Author

Wang, Lipo, Chen, Ke, Ong, Yew, Kim, Sun, Shin, Kyung-shik, and Park, Kyungdo

Abstract

This study investigates the effectiveness of support vector machines (SVM) approach in detecting the underlying data pattern for the credit card customer churn analysis. This article introduces a relatively new machine learning technique, SVM, to the customer churning problem in attempt to provide a model with better prediction accuracy. To compare the performance of the proposed model, we used a widely adopted and applied Artificial Intelligence (AI) method, back-propagation neural networks (BPN) as a benchmark. The results demonstrate that SVM outperforms BPN. We also examine the effect of the variability in performance with respect to various values of parameters in SVM. [ABSTRACT FROM AUTHOR]

Published
2005
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46. Time Delay Neural Networks and Genetic Algorithms for Detecting Temporal Patterns in Stock Markets.

Author

Wang, Lipo, Chen, Ke, Ong, Yew, Kim, Hyun-jung, Shin, Kyung-shik, and Park, Kyungdo

Abstract

This study investigates the effectiveness of a hybrid approach with the time delay neural networks (TDNNs) and the genetic algorithms (GAs) in detecting temporal patterns for stock market prediction tasks. Since TDNN is a multi-layer, feed-forward network whose hidden neurons and output neurons are replicated across time, it has one more estimate of time delays in addition to a number of control variables of the artificial neural network (ANN) design. To estimate these many aspects of the TDNN design, a general method based on trial and error along with various heuristics or statistical techniques is proposed. However, for the reason that determining time delays or network architectural factors in a stand-alone mode doesn't guarantee the illuminating improvement of the performance for building the TDNN models, we apply GAs to support optimization of time delays and network architectural factors simultaneously for the TDNN model. The results show that the accuracy of the integrated approach proposed for this study is higher than that of the standard TDNN and the recurrent neural networks (RNNs). [ABSTRACT FROM AUTHOR]

Published
2005
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