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ORCIDChih-Jen Lin
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- affiliation: National Taiwan University, Taipei, Taiwan
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2020 – today
- 2023
[i6]Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap, Stefan Winkler, Shao-Syuan Huang, Jie-Jyun Liu, Chih-Jen Lin:
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification. CoRR abs/2304.13998 (2023)- 2022
- [j66]Ching-Pei Lee, Po-Wei Wang, Chih-Jen Lin:
Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization. Math. Program. Comput. 14(3): 543-591 (2022) - [j65]Leonardo Galli
, Chih-Jen Lin:
A Study on Truncated Newton Methods for Linear Classification. IEEE Trans. Neural Networks Learn. Syst. 33(7): 2828-2841 (2022) - [c61]Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen, Kai-Chin Hsu, I-Feng Wu, Ming-Feng Tsai, Chih-Jen Lin:
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations. AAAI 2022: 7479-7487 - [c60]Yaxu Liu, Jui-Nan Yen, Bo-Wen Yuan, Rundong Shi, Peng Yan, Chih-Jen Lin:
Practical Counterfactual Policy Learning for Top-K Recommendations. KDD 2022: 1141-1151 - [c59]Si-An Chen, Jie-Jyun Liu, Tsung-Han Yang, Hsuan-Tien Lin, Chih-Jen Lin:
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN. NAACL-HLT 2022: 1987-2000 - 2021
- [j64]Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone:
A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems. J. Mach. Learn. Res. 22: 23:1-23:38 (2021) - [c58]Jie-Jyun Liu, Tsung-Han Yang, Si-An Chen, Chih-Jen Lin:
Parameter Selection: Why We Should Pay More Attention to It. ACL/IJCNLP (2) 2021: 825-830 - [c57]Jui-Nan Yen, Chih-Jen Lin:
Limited-memory Common-directions Method With Subsampled Newton Directions for Large-scale Linear Classification. ICDM 2021: 1457-1462 - [c56]Bo-Wen Yuan, Yu-Sheng Li, Pengrui Quan, Chih-Jen Lin:
Efficient Optimization Methods for Extreme Similarity Learning with Nonlinear Embeddings. KDD 2021: 2093-2103 - [i5]Jie-Jyun Liu, Tsung-Han Yang, Si-An Chen, Chih-Jen Lin:
Parameter Selection: Why We Should Pay More Attention to It. CoRR abs/2107.05393 (2021) - [i4]Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen, Kai-Chin Hsu, I-Feng Wu, Ming-Feng Tsai, Chih-Jen Lin:
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations. CoRR abs/2112.04274 (2021) - 2020
- [j63]Chien-Chih Wang, Kent Loong Tan, Chih-Jen Lin:
Newton Methods for Convolutional Neural Networks. ACM Trans. Intell. Syst. Technol. 11(2): 19:1-19:30 (2020) - [j62]Hui Xiong, Chih-Jen Lin:
Introduction to the Special Issue on the Best Papers from KDD 2018. ACM Trans. Knowl. Discov. Data 14(5): 51e:1-51e:2 (2020) - [j61]Jui-Yang Hsia, Chih-Jen Lin:
Parameter Selection for Linear Support Vector Regression. IEEE Trans. Neural Networks Learn. Syst. 31(12): 5639-5644 (2020) - [c55]Chih-Yao Chang, Xing Tang, Bo-Wen Yuan, Jui-Yang Hsia, Zhirong Liu, Zhenhua Dong, Xiuqiang He, Chih-Jen Lin:
AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction. MDM 2020: 202-209 - [c54]Bo-Wen Yuan, Yaxu Liu, Jui-Yang Hsia, Zhenhua Dong, Chih-Jen Lin:
Unbiased Ad Click Prediction for Position-aware Advertising Systems. RecSys 2020: 368-377 - [c53]Chi-Cheng Chiu, Pin-Yen Lin, Chih-Jen Lin:
Two-variable Dual Coordinate Descent Methods for Linear SVM with/without the Bias Term. SDM 2020: 163-171 - [c52]Hung-Yi Chou, Pin-Yen Lin, Chih-Jen Lin:
Dual Coordinate-Descent Methods for Linear One-Class SVM and SVDD. SDM 2020: 181-189 - [i3]Bo-Wen Yuan, Yu-Sheng Li, Pengrui Quan, Chih-Jen Lin:
An Efficient Newton Method for Extreme Similarity Learning with Nonlinear Embeddings. CoRR abs/2010.13511 (2020)
2010 – 2019
- 2019
- [j60]Po-Wei Wang, Ching-Pei Lee, Chih-Jen Lin:
The Common-directions Method for Regularized Empirical Risk Minimization. J. Mach. Learn. Res. 20: 58:1-58:49 (2019) - [c51]Bo-Wen Yuan, Jui-Yang Hsia, Meng-Yuan Yang, Hong Zhu, Chih-Yao Chang, Zhenhua Dong, Chih-Jen Lin:
Improving Ad Click Prediction by Considering Non-displayed Events. CIKM 2019: 329-338 - 2018
- [j59]Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S. Sathiya Keerthi, Dhruv Mahajan, S. Sundararajan, Chih-Jen Lin:
Distributed Newton Methods for Deep Neural Networks. Neural Comput. 30(6) (2018) - [j58]Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Chih-Jen Lin:
An Efficient Alternating Newton Method for Learning Factorization Machines. ACM Trans. Intell. Syst. Technol. 9(6): 72:1-72:31 (2018) - [c50]Chih-Yang Hsia, Wei-Lin Chiang, Chih-Jen Lin:
Preconditioned Conjugate Gradient Methods in Truncated Newton Frameworks for Large-scale Linear Classification. ACML 2018: 312-326 - [c49]Yong Zhuang, Yu-Chin Juan, Guo-Xun Yuan, Chih-Jen Lin:
Naive Parallelization of Coordinate Descent Methods and an Application on Multi-core L1-regularized Classification. CIKM 2018: 1103-1112 - [c48]Wei-Lin Chiang, Yu-Sheng Li, Ching-Pei Lee, Chih-Jen Lin:
Limited-memory Common-directions Method for Distributed Ll-regularized Linear Classification. SDM 2018: 504-512 - [i2]Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S. Sathiya Keerthi, Dhruv Mahajan, S. Sundararajan, Chih-Jen Lin:
Distributed Newton Methods for Deep Neural Networks. CoRR abs/1802.00130 (2018) - [i1]Chien-Chih Wang, Kent Loong Tan, Chih-Jen Lin:
Newton Methods for Convolutional Neural Networks. CoRR abs/1811.06100 (2018) - 2017
- [c47]Hsiang-Fu Yu, Hsin-Yuan Huang, Inderjit S. Dhillon, Chih-Jen Lin:
A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information. AAAI 2017: 2845-2851 - [c46]Chih-Yang Hsia, Ya Zhu, Chih-Jen Lin:
A Study on Trust Region Update Rules in Newton Methods for Large-scale Linear Classification. ACML 2017: 33-48 - [c45]Hsiang-Fu Yu, Mikhail Bilenko, Chih-Jen Lin:
Selection of Negative Samples for One-class Matrix Factorization. SDM 2017: 363-371 - [c44]Ching-Pei Lee, Po-Wei Wang, Weizhu Chen, Chih-Jen Lin:
Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization. SDM 2017: 732-740 - 2016
- [j57]Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin:
LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems. J. Mach. Learn. Res. 17: 86:1-86:5 (2016) - [c43]Wei-Lin Chiang, Mu-Chu Lee, Chih-Jen Lin:
Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments. KDD 2016: 1485-1494 - [c42]Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin:
Field-aware Factorization Machines for CTR Prediction. RecSys 2016: 43-50 - [c41]Hsin-Yuan Huang, Chih-Jen Lin:
Linear and Kernel Classification: When to Use Which? SDM 2016: 216-224 - 2015
- [j56]Chun-Liang Li, Yu-Chuan Su, Ting-Wei Lin, Cheng-Hao Tsai, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Chun-Pai Yang, Cheng-Xia Chang, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013. J. Mach. Learn. Res. 16: 2921-2947 (2015) - [j55]Chien-Chih Wang, Chun-Heng Huang, Chih-Jen Lin:
Subsampled Hessian Newton Methods for Supervised Learning. Neural Comput. 27(8): 1766-1795 (2015) - [j54]Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin:
A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems. ACM Trans. Intell. Syst. Technol. 6(1): 2:1-2:24 (2015) - [c40]Mu-Chu Lee, Wei-Lin Chiang, Chih-Jen Lin:
Fast Matrix-Vector Multiplications for Large-Scale Logistic Regression on Shared-Memory Systems. ICDM 2015: 835-840 - [c39]Bo-Yu Chu, Chia-Hua Ho, Cheng-Hao Tsai, Chieh-Yen Lin, Chih-Jen Lin:
Warm Start for Parameter Selection of Linear Classifiers. KDD 2015: 149-158 - [c38]Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin:
A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization. PAKDD (1) 2015: 442-455 - [c37]Yong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, Chih-Jen Lin:
Distributed Newton Methods for Regularized Logistic Regression. PAKDD (2) 2015: 690-703 - 2014
- [j53]Po-Wei Wang, Chih-Jen Lin:
Iteration complexity of feasible descent methods for convex optimization. J. Mach. Learn. Res. 15(1): 1523-1548 (2014) - [j52]Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Effective string processing and matching for author disambiguation. J. Mach. Learn. Res. 15(1): 3037-3064 (2014) - [j51]Ching-Pei Lee, Chih-Jen Lin:
Large-Scale Linear RankSVM. Neural Comput. 26(4): 781-817 (2014) - [j50]Meng-Chieh Yu, Tong Yu, Shao-Chen Wang, Chih-Jen Lin, Edward Y. Chang:
Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes. Proc. VLDB Endow. 7(13): 1429-1440 (2014) - [c36]Chieh-Yen Lin, Cheng-Hao Tsai, Ching-Pei Lee, Chih-Jen Lin:
Large-scale logistic regression and linear support vector machines using spark. IEEE BigData 2014: 519-528 - [c35]Cheng-Hao Tsai, Chieh-Yen Lin, Chih-Jen Lin:
Incremental and decremental training for linear classification. KDD 2014: 343-352 - [c34]Tzu-Ming Kuo, Ching-Pei Lee, Chih-Jen Lin:
Large-scale Kernel RankSVM. SDM 2014: 812-820 - [p2]Po-Wei Wang, Chih-Jen Lin:
Support Vector Machines. Data Classification: Algorithms and Applications 2014: 187-204 - 2013
- [j49]Ching-Pei Lee, Chih-Jen Lin:
A Study on L2-Loss (Squared Hinge-Loss) Multiclass SVM. Neural Comput. 25(5): 1302-1323 (2013) - [c33]Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan:
Large-Scale Video Summarization Using Web-Image Priors. CVPR 2013: 2698-2705 - [c32]Raffay Hamid, Dennis DeCoste, Chih-Jen Lin:
Dense Non-rigid Point-Matching Using Random Projections. CVPR 2013: 2914-2921 - [c31]Chun-Liang Li, Yu-Chuan Su, Ting-Wei Lin, Cheng-Hao Tsai, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Chun-Pai Yang, Cheng-Xia Chang, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-de Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Combination of feature engineering and ranking models for paper-author identification in KDD Cup 2013. KDD Cup 2013: 2:1-2:7 - [c30]Wei-Sheng Chin, Yu-Chin Juan, Yong Zhuang, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Effective string processing and matching for author disambiguation. KDD Cup 2013: 7:1-7:9 - [c29]Yong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, Chih-Jen Lin:
A fast parallel SGD for matrix factorization in shared memory systems. RecSys 2013: 249-256 - 2012
- [j48]Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin:
An Improved GLMNET for L1-regularized Logistic Regression. J. Mach. Learn. Res. 13: 1999-2030 (2012) - [j47]Chia-Hua Ho, Chih-Jen Lin:
Large-scale linear support vector regression. J. Mach. Learn. Res. 13: 3323-3348 (2012) - [j46]Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin:
Recent Advances of Large-Scale Linear Classification. Proc. IEEE 100(9): 2584-2603 (2012) - [j45]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large Linear Classification When Data Cannot Fit in Memory. ACM Trans. Knowl. Discov. Data 5(4): 23:1-23:23 (2012) - [c28]Chih-Jen Lin:
Experiences and lessons in developing industry-strength machine learning and data mining software. KDD 2012: 1176 - [c27]Po-Lung Chen, Chen-Tse Tsai, Yao-Nan Chen, Ku-Chun Chou, Chun-Liang Li, Cheng-Hao Tsai, Kuan-Wei Wu, Yu-Cheng Chou, Chung-Yi Li, Wei-Shih Lin, Shu-Hao Yu, Rong-Bing Chiu, Chieh-Yen Lin, Chien-Chih Wang, Po-Wei Wang, Wei-Lun Su, Chen-Hung Wu, Tsung-Ting Kuo, Todd G. McKenzie, Ya-Hsuan Chang, Chun-Sung Ferng, Chia-Mau Ni, Hsuan-Tien Lin, Chih-Jen Lin, Shou-De Lin:
A Linear Ensemble of Individual and Blended Models for Music Rating Prediction. KDD Cup 2012: 21-60 - [c26]Todd G. McKenzie, Chun-Sung Ferng, Yao-Nan Chen, Chun-Liang Li, Cheng-Hao Tsai, Kuan-Wei Wu, Ya-Hsuan Chang, Chung-Yi Li, Wei-Shih Lin, Shu-Hao Yu, Chieh-Yen Lin, Po-Wei Wang, Chia-Mau Ni, Wei-Lun Su, Tsung-Ting Kuo, Chen-Tse Tsai, Po-Lung Chen, Rong-Bing Chiu, Ku-Chun Chou, Yu-Cheng Chou, Chien-Chih Wang, Chen-Hung Wu, Hsuan-Tien Lin, Chih-Jen Lin, Shou-De Lin:
Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation. KDD Cup 2012: 101-135 - 2011
- [j44]Ruby C. Weng, Chih-Jen Lin:
A Bayesian Approximation Method for Online Ranking. J. Mach. Learn. Res. 12: 267-300 (2011) - [j43]Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin:
Dual coordinate descent methods for logistic regression and maximum entropy models. Mach. Learn. 85(1-2): 41-75 (2011) - [j42]Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen Lin, Edward Y. Chang:
Parallel Spectral Clustering in Distributed Systems. IEEE Trans. Pattern Anal. Mach. Intell. 33(3): 568-586 (2011) - [j41]Chih-Chung Chang, Chih-Jen Lin:
LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3): 27:1-27:27 (2011) - [c25]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large Linear Classification When Data Cannot Fit in Memory. IJCAI 2011: 2777-2782 - [c24]Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin:
An improved GLMNET for l1-regularized logistic regression. KDD 2011: 33-41 - 2010
- [j40]Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models. J. Mach. Learn. Res. 11: 815-848 (2010) - [j39]Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin:
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. J. Mach. Learn. Res. 11: 1471-1490 (2010) - [j38]Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification. J. Mach. Learn. Res. 11: 3183-3234 (2010) - [c23]Tsung-Ting Kuo, Jung-Jung Yeh, Chih-Jen Lin, Shou-De Lin:
Designing, Analyzing and Exploiting Stake-Based Social Networks. ASONAM 2010: 402-403 - [c22]Ming-Hen Tsai, Chia-Hua Ho, Chih-Jen Lin:
Active learning strategies using SVMs. IJCNN 2010: 1-8 - [c21]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large linear classification when data cannot fit in memory. KDD 2010: 833-842
2000 – 2009
- 2009
- [c20]Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy. ACL/IJCNLP (2) 2009: 285-288 - [c19]Hung-Yi Lo, Kai-Wei Chang, Shang-Tse Chen, Tsung-Hsien Chiang, Chun-Sung Ferng, Cho-Jui Hsieh, Yi-Kuang Ko, Tsung-Ting Kuo, Hung-Che Lai, Ken-Yi Lin, Chia-Hsuan Wang, Hsiang-Fu Yu, Chih-Jen Lin, Hsuan-Tien Lin, Shou-De Lin:
An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes. KDD Cup 2009: 57-64 - 2008
- [j37]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. J. Biomed. Informatics 41(4): 570-579 (2008) - [j36]Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi:
Trust Region Newton Method for Logistic Regression. J. Mach. Learn. Res. 9: 627-650 (2008) - [j35]Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines. J. Mach. Learn. Res. 9: 1369-1398 (2008) - [j34]Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin:
LIBLINEAR: A Library for Large Linear Classification. J. Mach. Learn. Res. 9: 1871-1874 (2008) - [c18]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 - [c17]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 - [c16]Yangqiu Song, WenYen Chen, Hongjie Bai, Chih-Jen Lin, Edward Y. Chang:
Parallel Spectral Clustering. ECML/PKDD (2) 2008: 374-389 - [c15]Yin-Wen Chang, Chih-Jen Lin:
Feature Ranking Using Linear SVM. WCCI Causation and Prediction Challenge 2008: 53-64 - 2007
- [j33]Hsuan-Tien Lin, Chih-Jen Lin, Ruby C. Weng:
A note on Platt's probabilistic outputs for support vector machines. Mach. Learn. 68(3): 267-276 (2007) - [j32]Chih-Jen Lin:
Projected Gradient Methods for Nonnegative Matrix Factorization. Neural Comput. 19(10): 2756-2779 (2007) - [j31]Chih-Jen Lin:
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization. IEEE Trans. Neural Networks 18(6): 1589-1596 (2007) - [c14]Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi:
Trust region Newton methods for large-scale logistic regression. ICML 2007: 561-568 - [c13]Ming-Fang Weng, Chun-Kang Chen, Yi-Hsuan Yang, Rong-En Fan, Yu-Ting Hsieh, Yung-Yu Chuang, Winston H. Hsu, Chih-Jen Lin:
The NTU Toolkit and Framework for High-Level Feature Detection at TRECVID 2007. TRECVID 2007 - 2006
- [j30]Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin:
Generalized Bradley-Terry Models and Multi-Class Probability Estimates. J. Mach. Learn. Res. 7: 85-115 (2006) - [j29]Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin:
A study on SMO-type decomposition methods for support vector machines. IEEE Trans. Neural Networks 17(4): 893-908 (2006) - [c12]Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng:
Ranking individuals by group comparisons. ICML 2006: 425-432 - [p1]Yi-Wei Chen, Chih-Jen Lin:
Combining SVMs with Various Feature Selection Strategies. Feature Extraction 2006: 315-324 - 2005
- [j28]Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin:
Working Set Selection Using Second Order Information for Training Support Vector Machines. J. Mach. Learn. Res. 6: 1889-1918 (2005) - [j27]Ming-Wei Chang, Chih-Jen Lin:
Leave-One-Out Bounds for Support Vector Regression Model Selection. Neural Comput. 17(5): 1188-1222 (2005) - [c11]Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin:
Training Support Vector Machines via SMO-Type Decomposition Methods. ALT 2005: 45-62 - [c10]Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin:
Training Support Vector Machines via SMO-Type Decomposition Methods. Discovery Science 2005: 15 - 2004
- [j26]Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng:
Probability Estimates for Multi-class Classification by Pairwise Coupling. J. Mach. Learn. Res. 5: 975-1005 (2004) - [j25]Wei-Chun Kao, Kai-Min Chung, Chia-Liang Sun, Chih-Jen Lin:
Decomposition Methods for Linear Support Vector Machines. Neural Comput. 16(8): 1689-1704 (2004) - [j24]Ming-Wei Chang, Chih-Jen Lin, Ruby Chiu-Hsing Weng:
Analysis of switching dynamics with competing support vector machines. IEEE Trans. Neural Networks 15(3): 720-727 (2004) - [c9]Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng:
A Generalized Bradley-Terry Model: From Group Competition to Individual Skill. NIPS 2004: 601-608 - 2003
- [j23]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) - [j22]S. Sathiya Keerthi, Chih-Jen Lin:
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Comput. 15(7): 1667-1689 (2003) - [j21]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 Comput. 15(11): 2643-2681 (2003) - [j20]Kuan-Ming Lin, Chih-Jen Lin:
A study on reduced support vector machines. IEEE Trans. Neural Networks 14(6): 1449-1459 (2003) - [c8]Kai-Min Chung, Wei-Chun Kao, Tony Sun, Chih-Jen Lin:
Decomposition methods for linear support vector machines. ICASSP (4) 2003: 868-871 - [c7]Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng:
Probability Estimates for Multi-Class Classification by Pairwise Coupling. NIPS 2003: 529-536 - 2002
- [j19]Chih-Wei Hsu, Chih-Jen Lin:
A Simple Decomposition Method for Support Vector Machines. Mach. Learn. 46(1-3): 291-314 (2002) - [j18]Shuo-Peng Liao, Hsuan-Tien Lin, Chih-Jen Lin:
A Note on the Decomposition Methods for Support Vector Regression. Neural Comput. 14(6): 1267-1281 (2002) - [j17]Chih-Chung Chang, Chih-Jen Lin:
Training v -Support Vector Regression: Theory and Algorithms. Neural Comput. 14(8): 1959-1977 (2002) - [j16]Chih-Jen Lin:
Asymptotic convergence of an SMO algorithm without any assumptions. IEEE Trans. Neural Networks 13(1): 248-250 (2002) - [j15]Chih-Wei Hsu, Chih-Jen Lin:
A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13(2): 415-425 (2002) - [j14]Chih-Jen Lin:
Errata to "On the convergence of the decomposition method for support vector machines". IEEE Trans. Neural Networks 13(4): 1025 (2002) - [j13]Chih-Jen Lin:
Errata to "A comparison of methods for multiclass support vector machines". IEEE Trans. Neural Networks 13(4): 1026-1027 (2002) - [j12]Chih-Jen Lin:
A formal analysis of stopping criteria of decomposition methods for support vector machines. IEEE Trans. Neural Networks 13(5): 1045-1052 (2002) - [c6]