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2020 – today
- 2024
[j29]Xiao-Li Wei
, Chunxia Zhang, Hongtao Wang, Chengli Tan, Deng Xiong, Baisong Jiang, Jiangshe Zhang, Sang-Woon Kim:
Seismic Data Interpolation via Denoising Diffusion Implicit Models With Coherence-Corrected Resampling. IEEE Trans. Geosci. Remote. Sens. 62: 1-17 (2024)- 2022
- [j28]Xiao-Li Wei, Chunxia Zhang, Sang-Woon Kim, Kai-Li Jing, Yong-Jun Wang, Shuang Xu, Zhuangzhuang Xie:
Seismic fault detection using convolutional neural networks with focal loss. Comput. Geosci. 158: 104968 (2022) - [j27]Xiao-Li Wei, Chunxia Zhang, Hongtao Wang, Zixiang Zhao, Xiong Deng, Shuang Xu, Jiangshe Zhang, Sang-Woon Kim:
Hybrid Loss-Guided Coarse-to-Fine Model for Seismic Data Consecutively Missing Trace Reconstruction. IEEE Trans. Geosci. Remote. Sens. 60: 1-15 (2022) - 2021
- [c41]Chun-Xia Zhang, Xiaoli Wei, Sang-Woon Kim:
Empirical Evaluation on Utilizing CNN-features for Seismic Patch Classification. ICPRAM 2021: 166-173 - 2020
- [j26]Chun-Xia Zhang, Sang-Woon Kim, Jiang-She Zhang:
On selective learning in stochastic stepwise ensembles. Int. J. Mach. Learn. Cybern. 11(1): 217-230 (2020)
2010 – 2019
- 2019
- [j25]Sang-Woon Kim, Joon-Min Gil:
Research paper classification systems based on TF-IDF and LDA schemes. Hum. centric Comput. Inf. Sci. 9: 30 (2019) - 2017
- [j24]B. John Oommen, Sang-Woon Kim:
Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information. Eng. Appl. Artif. Intell. 63: 69-84 (2017) - [j23]Thanh-Binh Le, Sugwon Hong, Sang-Woon Kim:
Multi-view based unlabeled data selection using feature transformation methods for semiboost learning. Neurocomputing 249: 277-289 (2017) - 2016
- [j22]Chun-Xia Zhang, Jiang-She Zhang, Sang-Woon Kim:
PBoostGA: pseudo-boosting genetic algorithm for variable ranking and selection. Comput. Stat. 31(4): 1237-1262 (2016) - [j21]Thanh-Binh Le, Sang-Woon Kim:
On measuring confidence levels using multiple views of feature set for useful unlabeled data selection. Neurocomputing 173: 1589-1601 (2016) - [j20]Yu-Seung Ma, Sang-Woon Kim:
Mutation testing cost reduction by clustering overlapped mutants. J. Syst. Softw. 115: 18-30 (2016) - [c40]B. John Oommen, Sang-Woon Kim:
On the Foundations of Multinomial Sequence Based Estimation. ICCCI (1) 2016: 218-229 - [c39]B. John Oommen, Sang-Woon Kim:
Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three. ICIAR 2016: 243-253 - [c38]Trung Hai Nguyen, Thanh-Binh Le, Sang-Woon Kim:
Choosing unlabeled examples for SemiBoost using modified cuckoo search algorithms. ICNC-FSKD 2016: 534-541 - 2015
- [j19]Thanh-Binh Le, Sang-Woon Kim:
Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines. Pattern Recognit. Lett. 60-61: 48-56 (2015) - [c37]Thanh-Binh Le, Sang-Woon Kim:
On Selecting Useful Unlabeled Data Using Multi-view Learning Techniques. ICPRAM (1) 2015: 157-164 - [c36]Thanh-Binh Le, Sang-Woon Kim:
Comparison of Adjusted Methods for Selecting Useful Unlabeled Data for Semi-Supervised Learning Algorithms. IEA/AIE 2015: 526-535 - 2014
- [j18]Sang-Woon Kim:
An empirical study on improving dissimilarity-based classifications using one-shot similarity measure. Digit. Signal Process. 27: 69-78 (2014) - [j17]Thanh-Binh Le, Sang-Woon Kim:
On incrementally using a small portion of strong unlabeled data for semi-supervised learning algorithms. Pattern Recognit. Lett. 41: 53-64 (2014) - [c35]Thanh-Binh Le, Sang-Woon Kim:
Simply recycled selection and incrementally reinforced selection methods applicable for semi-supervised learning algorithms. ICEIC 2014: 1-2 - [c34]Thanh-Binh Le, Sang-Woon Kim:
On Selecting Helpful Unlabeled Data for Improving Semi-Supervised Support Vector Machines. ICPRAM 2014: 48-59 - [c33]Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, Marco Loog:
Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance. S+SSPR 2014: 183-192 - 2013
- [j16]Sang-Woon Kim, Yu-Seung Ma, Yong Rae Kwon:
Combining weak and strong mutation for a noninterpretive Java mutation system. Softw. Test. Verification Reliab. 23(8): 647-668 (2013) - [c32]Sang-Woon Kim:
On using Additional Unlabeled Data for Improving Dissimilarity-Based Classifications. ICPRAM 2013: 132-137 - 2012
- [j15]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to optimize locally linear reconstruction methods. Pattern Recognit. 45(1): 498-511 (2012) - [c31]Thanh-Binh Le, Sang-Woon Kim:
On Improving Semi-supervised Marginboost Incrementally using Strong Unlabeled Data. ICPRAM (1) 2012: 265-268 - 2011
- [j14]Sang-Woon Kim:
An empirical evaluation on dimensionality reduction schemes for dissimilarity-based classifications. Pattern Recognit. Lett. 32(6): 816-823 (2011) - [c30]Sang-Woon Kim, Robert P. W. Duin:
Dissimilarity-Based Classifications in Eigenspaces. CIARP 2011: 425-432 - [c29]Evensen E. Masaki, Sang-Woon Kim:
An Improvement of Dissimilarity-Based Classifications Using SIFT Algorithm. PReMI 2011: 74-79 - [e1]César San Martín, Sang-Woon Kim:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Pucón, Chile, November 15-18, 2011. Proceedings. Lecture Notes in Computer Science 7042, Springer 2011, ISBN 978-3-642-25084-2 [contents] - 2010
- [j13]Sang-Woon Kim:
A pre-clustering technique for optimizing subclass discriminant analysis. Pattern Recognit. Lett. 31(6): 462-468 (2010) - [c28]Sang-Woon Kim, Seunghwan Kim:
A Multiple Combining Method for Optimizing Dissimilarity-Based Classification. ACIIDS (2) 2010: 310-319 - [c27]Sang-Woon Kim, B. John Oommen:
On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes. Australasian Conference on Artificial Intelligence 2010: 153-163 - [c26]Sang-Woon Kim, Robert P. W. Duin:
On Improving Dissimilarity-Based Classifications Using a Statistical Similarity Measure. CIARP 2010: 418-425 - [c25]Sang-Woon Kim:
On Reducing Dimensionality of Dissimilarity Matrices for Optimizing DBC - An Experimental Comparison. ICAART (1) 2010: 235-240 - [c24]Sang-Woon Kim, Robert P. W. Duin:
An Empirical Comparison of Kernel-Based and Dissimilarity-Based Feature Spaces. SSPR/SPR 2010: 559-568
2000 – 2009
- 2009
- [j12]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures. Pattern Recognit. 42(11): 2695-2704 (2009) - [c23]Sang-Woon Kim, Robert P. W. Duin:
A Combine-Correct-Combine Scheme for Optimizing Dissimilarity-Based Classifiers. CIARP 2009: 425-432 - 2008
- [j11]B. John Oommen, Sang-Woon Kim, M. T. Samuel, Ole-Christoffer Granmo:
A Solution to the Stochastic Point Location Problem in Metalevel Nonstationary Environments. IEEE Trans. Syst. Man Cybern. Part B 38(2): 466-476 (2008) - [j10]Sang-Woon Kim, B. John Oommen:
On Using Prototype Reduction Schemes to Optimize Kernel-Based Fisher Discriminant Analysis. IEEE Trans. Syst. Man Cybern. Part B 38(2): 564-570 (2008) - [c22]Sang-Woon Kim, B. John Oommen:
A Fast Computation of Inter-class Overlap Measures Using Prototype Reduction Schemes. Canadian AI 2008: 173-184 - [c21]Sang-Woon Kim:
On Optimizing Subclass Discriminant Analysis Using a Pre-clustering Technique. CIARP 2008: 292-300 - [c20]Sang-Woon Kim, Jian Gao:
On Using Dimensionality Reduction Schemes to Optimize Dissimilarity-Based Classifiers. CIARP 2008: 309-316 - [c19]Sang-Woon Kim, Jian Gao:
A Dynamic Programming Technique for Optimizing Dissimilarity-Based Classifiers. SSPR/SPR 2008: 654-663 - 2007
- [j9]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to optimize dissimilarity-based classification. Pattern Recognit. 40(11): 2946-2957 (2007) - [j8]B. John Oommen, Sang-Woon Kim, Geir Horn:
On the estimation of independent binomial random variables using occurrence and sequential information. Pattern Recognit. 40(11): 3263-3276 (2007) - [c18]Sang-Woon Kim, Robert P. W. Duin:
On Combining Dissimilarity-Based Classifiers to Solve the Small Sample Size Problem for Appearance-Based Face Recognition. Canadian AI 2007: 110-121 - [c17]Sang-Woon Kim, Robert P. W. Duin:
On Using a Pre-clustering Technique to Optimize LDA-Based Classifiers for Appearance-Based Face Recognition. CIARP 2007: 466-476 - [c16]B. John Oommen, Sang-Woon Kim, Mathew Samuel, Ole-Christoffer Granmo:
Stochastic Point Location in Non-stationary Environments and Its Applications. IEA/AIE 2007: 845-854 - 2006
- [j7]Sang-Woon Kim, B. John Oommen:
Prototype reduction schemes applicable for non-stationary data sets. Pattern Recognit. 39(2): 209-222 (2006) - [c15]Sang-Woon Kim:
On Using a Dissimilarity Representation Method to Solve the Small Sample Size Problem for Face Recognition. ACIVS 2006: 1174-1185 - [c14]Sang-Woon Kim, B. John Oommen:
On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes. ICIAR (1) 2006: 15-28 - [c13]Sang-Woon Kim, Soo-Hwan Oh:
On Adaptively Learning HMM-Based Classifiers Using Split-Merge Operations. IEA/AIE 2006: 668-673 - [c12]Sang-Woon Kim:
Optimizing Dissimilarity-Based Classifiers Using a Newly Modified Hausdorff Distance. PKAW 2006: 177-186 - [c11]B. John Oommen, Sang-Woon Kim, Geir Horn:
On the Theory and Applications of Sequence Based Estimation of Independent Binomial Random Variables. SSPR/SPR 2006: 8-21 - [c10]Sang-Woon Kim, B. John Oommen:
On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype Reduction Schemes. SSPR/SPR 2006: 826-834 - 2005
- [j6]Sang-Woon Kim, B. John Oommen:
On Utilizing Search Methods to Select Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 27(1): 136-141 (2005) - [j5]Sang-Woon Kim, B. John Oommen:
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods. IEEE Trans. Pattern Anal. Mach. Intell. 27(3): 455-460 (2005) - [c9]Sang-Woon Kim, B. John Oommen:
Time-Varying Prototype Reduction Schemes Applicable for Non-stationary Data Sets. Australian Conference on Artificial Intelligence 2005: 614-623 - 2004
- [j4]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods. Pattern Recognit. 37(2): 227-239 (2004) - [j3]Sang-Woon Kim, B. John Oommen:
Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets. IEEE Trans. Syst. Man Cybern. Part B 34(3): 1384-1397 (2004) - [c8]Sang-Woon Kim, B. John Oommen:
Selecting Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers Using Intelligent Search Methods. Australian Conference on Artificial Intelligence 2004: 1115-1121 - [c7]Sang-Woon Kim, Zhe-Xue Li, Yoshinao Aoki:
On intelligent avatar communication using Korean, Chinese and Japanese sign-languages: an overview. ICARCV 2004: 747-752 - 2003
- [j2]Sang-Woon Kim, B. John Oommen:
A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Anal. Appl. 6(3): 232-244 (2003) - [j1]Sang-Woon Kim, B. John Oommen:
Enhancing prototype reduction schemes with LVQ3-type algorithms. Pattern Recognit. 36(5): 1083-1093 (2003) - [c6]Sang-Woon Kim, B. John Oommen:
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods. Australian Conference on Artificial Intelligence 2003: 783-795 - 2002
- [c5]Sang-Woon Kim, B. John Oommen:
Optimizing Kernel-Based Nonlinear Subspace Methods Using Prototype Reduction Schemes. Australian Joint Conference on Artificial Intelligence 2002: 155-166 - [c4]Sang-Woon Kim, B. John Oommen:
On Utilizing LVQ3-Type Algorithms to Enhance Prototype Reduction Schemes. PRIS 2002: 242-256 - [c3]Sang-Woon Kim, B. John Oommen:
Creative prototype reduction schemes: a taxonomy and ranking. SMC (2) 2002: 6 - [c2]Sang-Woon Kim, B. John Oommen:
Recursive Prototype Reduction Schemes Applicable for Large Data Sets. SSPR/SPR 2002: 528-537
1990 – 1999
- 1999
- [c1]Sang-Woon Kim, Jong-Woo Lee, Ji-Yong Oh, Yoshinao Aoki:
A Comparative Study on the Sign-Language Communication Systems Between Korea and Japan Through 2D and 3D Character Models on the Internet. ICIP (4) 1999: 227-231
Coauthor Index
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