2008 | ||
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37 | EE | Yangqiu Song, WenYen Chen, Hongjie Bai, Chih-Jen Lin, Edward Y. Chang: Parallel Spectral Clustering. ECML/PKDD (2) 2008: 374-389 |
36 | EE | Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan: A dual coordinate descent method for large-scale linear SVM. ICML 2008: 408-415 |
35 | EE | S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin: A sequential dual method for large scale multi-class linear svms. KDD 2008: 408-416 |
34 | EE | Hsi-Che Liu, Chien-Yu Chen, Yu-Ting Liu, Cheng-Bang Chu, Der-Cherng Liang, Lee-Yung Shih, Chih-Jen Lin: Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods. Journal of Biomedical Informatics 41(4): 570-579 (2008) |
2007 | ||
33 | EE | Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi: Trust region Newton methods for large-scale logistic regression. ICML 2007: 561-568 |
32 | EE | Chih-Jen Lin: On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization. IEEE Transactions on Neural Networks 18(6): 1589-1596 (2007) |
31 | EE | Hsuan-Tien Lin, Chih-Jen Lin, Ruby C. Weng: A note on Platt's probabilistic outputs for support vector machines. Machine Learning 68(3): 267-276 (2007) |
30 | EE | Chih-Jen Lin: Projected Gradient Methods for Nonnegative Matrix Factorization. Neural Computation 19(10): 2756-2779 (2007) |
2006 | ||
29 | EE | Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng: Ranking individuals by group comparisons. ICML 2006: 425-432 |
28 | EE | Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin: A study on SMO-type decomposition methods for support vector machines. IEEE Transactions on Neural Networks 17(4): 893-908 (2006) |
27 | EE | Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin: Generalized Bradley-Terry Models and Multi-Class Probability Estimates. Journal of Machine Learning Research 7: 85-115 (2006) |
2005 | ||
26 | EE | Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin: Training Support Vector Machines via SMO-Type Decomposition Methods. ALT 2005: 45-62 |
25 | EE | Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin: Training Support Vector Machines via SMO-Type Decomposition Methods. Discovery Science 2005: 15 |
24 | EE | Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin: Working Set Selection Using Second Order Information for Training Support Vector Machines. Journal of Machine Learning Research 6: 1889-1918 (2005) |
23 | EE | Ming-Wei Chang, Chih-Jen Lin: Leave-One-Out Bounds for Support Vector Regression Model Selection. Neural Computation 17(5): 1188-1222 (2005) |
2004 | ||
22 | EE | Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng: A Generalized Bradley-Terry Model: From Group Competition to Individual Skill. NIPS 2004 |
21 | EE | Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng: Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research 5: 975-1005 (2004) |
20 | EE | Wei-Chun Kao, Kai-Min Chung, Chia-Liang Sun, Chih-Jen Lin: Decomposition Methods for Linear Support Vector Machines. Neural Computation 16(8): 1689-1704 (2004) |
2003 | ||
19 | EE | Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng: Probability Estimates for Multi-Class Classification by Pairwise Coupling. NIPS 2003 |
18 | EE | Kai-Min Chung, Wei-Chun Kao, Chia-Liang Sun, Li-Lun Wang, Chih-Jen Lin: Radius Margin Bounds for Support Vector Machines with the RBF Kernel. Neural Computation 15(11): 2643-2681 (2003) |
17 | EE | S. Sathiya Keerthi, Chih-Jen Lin: Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation 15(7): 1667-1689 (2003) |
16 | EE | Colin Campbell, Chih-Jen Lin, S. Sathiya Keerthi, V. David Sánchez A.: Special issue on support vector machines. Neurocomputing 55(1-2): 1-3 (2003) |
2002 | ||
15 | EE | Ming-Wei Chang, Chih-Jen Lin, Ruby C. Weng: Analysis of Nonstationary Time Series Using Support Vector Machines. SVM 2002: 160-170 |
14 | Chih-Wei Hsu, Chih-Jen Lin: A Simple Decomposition Method for Support Vector Machines. Machine Learning 46(1-3): 291-314 (2002) | |
13 | EE | Shuo-Peng Liao, Hsuan-Tien Lin, Chih-Jen Lin: A Note on the Decomposition Methods for Support Vector Regression. Neural Computation 14(6): 1267-1281 (2002) |
12 | EE | Chih-Chung Chang, Chih-Jen Lin: Training v -Support Vector Regression: Theory and Algorithms. Neural Computation 14(8): 1959-1977 (2002) |
2001 | ||
11 | EE | Soon-Yi Wu, Shu-Cherng Fang, Chih-Jen Lin: Solving General Capacity Problem by Relaxed Cutting Plane Approach. Annals OR 103(1-4): 193-211 (2001) |
10 | Jinn-Moon Yang, Jorng-Tzong Horng, Chih-Jen Lin, Cheng-Yan Kao: Optical Coating Designs Using the Family Competition Evolutionary Algorithm. Evolutionary Computation 9(4): 421-443 (2001) | |
9 | Chih-Jen Lin: Formulations of Support Vector Machines: A Note from an Optimization Point of View. Neural Computation 13(2): 307-317 (2001) | |
8 | Chih-Chung Chang, Chih-Jen Lin: Training nu-Support Vector Classifiers: Theory and Algorithms. Neural Computation 13(9): 2119-2147 (2001) | |
1998 | ||
7 | EE | Huan-Chih Tsai, Kwang-Ting Cheng, Chih-Jen Lin, Sudipta Bhawmik: Efficient test-point selection for scan-based BIST. IEEE Trans. VLSI Syst. 6(4): 667-676 (1998) |
1997 | ||
6 | EE | Huan-Chih Tsai, Kwang-Ting Cheng, Chih-Jen Lin, Sudipta Bhawmik: A Hybrid Algorithm for Test Point Selection for Scan-Based BIST. DAC 1997: 478-483 |
1995 | ||
5 | Kwang-Ting Cheng, Chih-Jen Lin: Timing-Driven Test Point Insertion for Full-Scan and Partial-Scan BIST. ITC 1995: 506-514 | |
4 | EE | Chih-Jen Lin, Yervant Zorian, Sudipta Bhawmik: Integration of partial scan and built-in self-test. J. Electronic Testing 7(1-2): 125-137 (1995) |
1993 | ||
3 | Chih-Jen Lin, Yervant Zorian, Sudipta Bhawmik: PSBIST: A Partial-Scan Based Built-In Self-Test Scheme. ITC 1993: 507-516 | |
2 | Ching-Wen Hsue, Chih-Jen Lin: Built-In Current Sensor for IDDQ Test in CMOS. ITC 1993: 635-641 | |
1991 | ||
1 | EE | Tapan J. Chakraborty, Sudipta Bhawmik, Robert Bencivenga, Chih-Jen Lin: Enhanced Controllability for IDDQ Test Sets Using Partial Scan. DAC 1991: 278-281 |