Chih-Jen Lin

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Incomplete Cholesky Factorizations with Limited Memory

Chih-Jen Lin, Jorge J. Moré, J. J. Moré, 1999, SIAM J. Sci. Comput..

A fast parallel SGD for matrix factorization in shared memory systems

Chih-Jen Lin, Wei-Sheng Chin, Yu-Chin Juan, 2013, RecSys.

Coordinate Descent Method for Large-scale L 2-loss Linear SVM

Cho-Jui Hsieh, Chih-Jen Lin, Kai-Wei Chang, 2008 .

Manuscript Number: 2187 Training ν-Support Vector Classifiers: Theory and Algorithms

Chih-Jen Lin, Chih-Chung Chang, 2022 .

1 Support Vector Machine Solvers

L. Bottou, Chih-Jen Lin, 2007 .

LIBSVM: A library for support vector machines

Chih-Jen Lin, Chih-Chung Chang, Chih-Jen Lin, 2011, TIST.

Linear Convergence of a Decomposition Method for Support Vector Machines

Chih-Jen Lin, 2001 .

Simple Probabilistic Predictions for Support Vector Regression

Chih-Jen Lin, R. C. Weng, 2004 .

Training v-Support Vector Classifiers: Theory and Algorithms

Chih-Jen Lin, Chih-Chung Chang, Chih-Chung Chang, 2001, Neural Computation.

LIBLINEAR: A Library for Large Linear Classification

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2008, J. Mach. Learn. Res..

Trust region Newton methods for large-scale logistic regression

Chih-Jen Lin, S. Sathiya Keerthi, Ruby C. Weng, 2007, ICML '07.

Decomposition Methods for Linear Support Vector Machines

Chih-Jen Lin, Wei-Chun Kao, Kai-Min Chung, 2004, Neural Computation.

Stopping Criteria of Decomposition Methods for Support Vector Machines: a Theoretical Justification

Chih-Jen Lin, 2001 .

A dual coordinate descent method for large-scale linear SVM

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2008, ICML '08.

Large Linear Classification When Data Cannot Fit in Memory

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2011, TKDD.

Iteration complexity of feasible descent methods for convex optimization

Chih-Jen Lin, Po-Wei Wang, Chih-Jen Lin, 2014, J. Mach. Learn. Res..

A comparison of methods for multiclass support vector machines

Chih-Jen Lin, Chih-Wei Hsu, Chih-Jen Lin, 2002, IEEE Trans. Neural Networks.

Newton's Method for Large Bound-Constrained Optimization Problems

Chih-Jen Lin, Jorge J. Moré, J. J. Moré, 1999, SIAM J. Optim..

Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2008, J. Mach. Learn. Res..

An Infeasible Start Predictor Corrector Method for Semi-deenite Linear Programming

R. Saigal, Chih-Jen Lin, 1995 .

A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2010, J. Mach. Learn. Res..

A Practical Guide to Support Vector Classication

Chih-Jen Lin, Chih-Chung Chang, Chih-Wei Hsu, 2008 .

Trust Region Newton Method for Logistic Regression

Chih-Jen Lin, S. Sathiya Keerthi, Ruby C. Weng, 2008, J. Mach. Learn. Res..

A formal analysis of stopping criteria of decomposition methods for support vector machines

Chih-Jen Lin, Chih-Jen Lin, 2002, IEEE Trans. Neural Networks.

Probability Estimates for Multi-class Classification by Pairwise Coupling

Chih-Jen Lin, Ruby C. Weng, Ting-Fan Wu, 2003, J. Mach. Learn. Res..

Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds

S. Lucidi, Chih-Jen Lin, M. Sciandrone, 2009 .

Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes

Chih-Jen Lin, Meng-Chieh Yu, Tong Yu, 2014, Proc. VLDB Endow..

A Comparison of Methods for Multi-class Support Vector Machines

Chih-Jen Lin, Chih-Wei Hsu, 2015 .

On the convergence of the decomposition method for support vector machines

Chih-Jen Lin, Chih-Jen Lin, Chih-Wei Hsu, 2001, IEEE Trans. Neural Networks.

Training nu-support vector regression: theory and algorithms.

Chih-Jen Lin, Chih-Chung Chang, 2002, Neural computation.

Distributed Newton Methods for Regularized Logistic Regression

Chih-Jen Lin, Wei-Sheng Chin, Yu-Chin Juan, 2015, PAKDD.

Large-Scale Video Summarization Using Web-Image Priors

Chih-Jen Lin, Neel Sundaresan, Raffay Hamid, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

A sequential dual method for large scale multi-class linear svms

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2008, KDD.

Large-scale logistic regression and linear support vector machines using spark

Cheng-Hao Tsai, Chieh-Yen Lin, Chih-Jen Lin, 2014, 2014 IEEE International Conference on Big Data (Big Data).

A Study on SMO-Type Decomposition Methods for Support Vector Machines

Chih-Jen Lin, Pai-Hsuen Chen, Rong-En Fan, 2006, IEEE Transactions on Neural Networks.

A Note on the Decomposition Methods for Support Vector Regression

Hsuan-Tien Lin, Chih-Jen Lin, Shuo-Peng Liao, 2001, Neural Computation.

A Linear Ensemble of Individual and Blended Models for Music Rating Prediction

Shou-De Lin, Chun-Liang Li, Hsuan-Tien Lin, 2012, KDD Cup.

The analysis of decomposition methods for support vector machines

Chih-Jen Lin, Chih-Chung Chang, Chih-Wei Hsu, 2000, IEEE Trans. Neural Networks Learn. Syst..

Feature Engineering and Classifier Ensemble for KDD Cup 2010

Shou-De Lin, Hsuan-Tien Lin, Chia-Hua Ho, 2010, KDD 2010.

Formulations of Support Vector Machines: A Note from an Optimization Point of View

Chih-Jen Lin, Chih-Jen Lin, 2001, Neural Computation.

Generalized Bradley-Terry Models and Multi-Class Probability Estimates

Chih-Jen Lin, Tzu-Kuo Huang, Ruby C. Weng, 2006, J. Mach. Learn. Res..

An Incomplete Cholesky Factorization for Dense Symmetric Positive Definite Matrices

R. Saigal, Chih-Jen Lin, 2000 .

Large-Scale Linear RankSVM

Chih-Jen Lin, Ching-Pei Lee, Chih-Jen Lin, 2014, Neural Computation.

A tutorial on?-support vector machines

Chih-Jen Lin, Pai-Hsuen Chen, Bernhard Sch lkopf, 2005 .

A Revisit to Support Vector Data Description

Chih-Jen Lin, Wei-Cheng Chang, Ching-pei Lee, 2015 .

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2009, ACL.

A note on Platt’s probabilistic outputs for support vector machines

Hsuan-Tien Lin, Chih-Jen Lin, Ruby C. Weng, 2007, Machine Learning.

Working Set Selection Using Second Order Information for Training Support Vector Machines

Chih-Jen Lin, Pai-Hsuen Chen, Rong-En Fan, 2005, J. Mach. Learn. Res..

Asymptotic convergence of an SMO algorithm without any assumptions

Chih-Jen Lin, Chih-Jen Lin, 2002, IEEE Trans. Neural Networks.

Projected Gradient Methods for Nonnegative Matrix Factorization

Chih-Jen Lin, Chih-Jen Lin, 2007, Neural Computation.

Analysis of switching dynamics with competing support vector machines

Ming-Wei Chang, Chih-Jen Lin, Ruby Chiu-Hsing Weng, 2004, IEEE Transactions on Neural Networks.

An improved GLMNET for l1-regularized logistic regression

Chia-Hua Ho, Chih-Jen Lin, Guo-Xun Yuan, 2011, J. Mach. Learn. Res..

Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001

Ming-Wei Chang, Chih-Jen Lin, Bo-Juen Chen, 2004, IEEE Transactions on Power Systems.

Leave-One-Out Bounds for Support Vector Regression Model Selection

Ming-Wei Chang, Chih-Jen Lin, Ming-Wei Chang, 2005, Neural Computation.

Feature Ranking Using Linear SVM

Chih-Jen Lin, Yin-Wen Chang, Chih-Jen Lin, 2008, WCCI Causation and Prediction Challenge.

PSC : Parallel Spectral Clustering

Chih-Jen Lin, Yangqiu Song, E. Chang, 2008 .

Decomposition Methods for Linear Support Vector Machines

Chih-Jen Lin, Kai-Min Chung, Tony Sun, 2003, Neural Computation.

Training and Testing Low-degree Polynomial Data Mappings via Linear SVM

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2010, J. Mach. Learn. Res..

A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization

Chih-Jen Lin, Wei-Sheng Chin, Yu-Chin Juan, 2015, PAKDD.

A Simple Decomposition Method for Support Vector Machines

Chih-Jen Lin, Chih-Wei Hsu, Chih-Jen Lin, 2002, Machine Learning.

Special issue on support vector machines

Chih-Jen Lin, Colin Campbell, S. Sathiya Keerthi, 2003, Neurocomputing.

Fast Matrix-Vector Multiplications for Large-Scale Logistic Regression on Shared-Memory Systems

Chih-Jen Lin, Wei-Lin Chiang, Mu-Chu Lee, 2015, 2015 IEEE International Conference on Data Mining.

Supplementary Materials for “Parameter Selection for Linear Support Vector Regression”

Chih-Jen Lin, Jui-Yang Hsia, 2018 .

A Dual Affine Scaling Based Algorithm for Solving Linear Semi-Infinite Programming Problems

Chih-Jen Lin, Shu-Cherng Fang, Soon-Yi Wu, 1994 .

Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization

Chih-Jen Lin, Po-Wei Wang, Ching-pei Lee, 2022, Mathematical Programming Computation.

A Study on Truncated Newton Methods for Linear Classification

Chih-Jen Lin, L. Galli, Leonardo Galli, 2021, IEEE Transactions on Neural Networks and Learning Systems.

Relaxed Cutting Plane Method for Solving Linear Semi-Infinite Programming Problems

Chih-Jen Lin, Shu-Cherng Fang, Soon-Yi Wu, 1998 .

First order methods in optimization Stream : Invited sessions

Chih-Jen Lin, F. Schoen, L. Palagi, 2022 .

Preconditioning Dense Linear Systems from Large-scale Semideenite Programming Problems

Chih-Jen Lin, 1998 .

Adaptive deterministic annealing for two applications: competing SVR of switching dynamics and travelling salesman problems

Ming-Wei Chang, Chih-Jen Lin, R. C. Weng, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Training ν-Support Vector Classifiers: Theory and Algorithms

Chih-Jen Lin, Chih-Chung Chang, 2001 .

A Predictor Corrector Method for Semi-deenite Linear Programming Abbreviated Title: Semi-deenite Linear Programming

R. Saigal, Chih-Jen Lin, 2008 .

A Guide to Support Vector Machines

Chih-Jen Lin, Chih-Jen Lin, 2006 .

Errata to "A comparison of methods for multiclass support vector machines"

Chih-Jen Lin, Chih-Jen Lin, 2002, IEEE Trans. Neural Networks.

Scaling Up Machine Learning: Large-Scale Spectral Clustering with Map Reduce and MPI

Edward Y. Chang, Chih-Jen Lin, Wen-Yen Chen, 2011 .

A predictor corrector method for semi-definite linear programming

Chih-Jen Lin, Romesh. Saigal, R. Saigal, 1995 .

Topological Fixture Synthesis Using Semide nite Programming

Chih-Jen Lin, 1999 .

Support Vector Machines

Chih-Jen Lin, Po-Wei Wang, Chih-Jen Lin, 2014, Data Classification: Algorithms and Applications.

Errata to "On the convergence of the decomposition method for support vector machines"

Chih-Jen Lin, Chih-Jen Lin, 2002, IEEE Trans. Neural Networks.

An infeasable start predictor corrector method for semi-definite linear programming

Chih-Jen Lin, Romesh. Saigal, R. Saigal, 1995 .

Solving quadratic semi-infinite programming problems by using relaxed cutting-plane scheme

Chih-Jen Lin, Shu-Cherng Fang, Soon-Yi Wu, 2001 .

Training nu-Support Vector Classifiers: Theory and Algorithms

Chih-Jen Lin, Chih-Chung Chang, Chih-Chung Chang, 2001, Neural Comput..

A Note on the Decomposition Methods for Support Vector Regression

Chih-Jen Lin, Hsuan-Tien Lin, Shuo-Peng Liao, 2001, Neural Computation.

A note on the decomposition methods for support vector regression

Chih-Jen Lin, Hsuan-Tien Lin, Shuo-Peng Liao, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

A Note on the Decomposition Methods for Support Vector Regression

Chih-Jen Lin, Hsuan-Tien Lin, Shuo-Peng Liao, 2001, Neural Computation.

On solving convex quadratic semi-infinite programming probelms

Chih-Jen Lin, S. Fang, Sy Wu, 1994 .

Large linear classification when data cannot fit in memory

Cho-Jui Hsieh, Chih-Jen Lin, Kai-Wei Chang, 2010, KDD.

One-class SVM Probabilistic Outputs

Chih-Jen Lin, Zhongyi Que, 2022 .

IJCNN 2001 challenge: generalization ability and text decoding

Chih-Jen Lin, Chih-Chung Chang, Chih-Jen Lin, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Limited-memory Common-directions Method With Subsampled Newton Directions for Large-scale Linear Classification

Chih-Jen Lin, Jui-Nan Yen, 2021, 2021 IEEE International Conference on Data Mining (ICDM).

The Common-directions Method for Regularized Empirical Risk Minimization

Chih-Jen Lin, Ching-Pei Lee, Po-Wei Wang, 2019, J. Mach. Learn. Res..

Support Vector Machines

Chih-Jen Lin, Po-Wei Wang, 2014, Data Classification: Algorithms and Applications.

Recent Advances of Large-Scale Linear Classification

Chia-Hua Ho, Chih-Jen Lin, Guo-Xun Yuan, 2012, Proceedings of the IEEE.

Recent Advances of Large-Scale Linear Classification This paper is a survey on development of optimization methods to construct linear classifiers suitable for large-scale applications; for some data, accuracy is close to that of nonlinear classifiers.

Chih-Jen Lin, Guo-Xun Yuan, Chia-Hua Ho, 2012 .

Product Title Classification versus Text Classification

Chia-Hua Ho, Chih-Jen Lin, Hsiang-Fu Yu, 2012 .

Parallel Spectral Clustering in Distributed Systems

Edward Y. Chang, Chih-Jen Lin, Wen-Yen Chen, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Training v-Support Vector Regression: Theory and Algorithms

Chih-Jen Lin, Chih-Chung Chang, Chih-Jen Lin, 2002, Neural Computation.

Large-scale linear support vector regression

Chia-Hua Ho, Chih-Jen Lin, Chih-Jen Lin, 2012, J. Mach. Learn. Res..

Combination of feature engineering and ranking models for paper-author identification in KDD Cup 2013

Shou-De Lin, Chun-Liang Li, Hsuan-Tien Lin, 2013, KDD Cup '13.

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

Chih-Jen Lin, S. Sathiya Keerthi, Chih-Jen Lin, 2003, Neural Computation.

Radius Margin Bounds for Support Vector Machines with the RBF Kernel

Chih-Jen Lin, Kai-Min Chung, Li-Lun Wang, 2002, Neural Computation.

Effective string processing and matching for author disambiguation

Shou-De Lin, Chun-Liang Li, Hsuan-Tien Lin, 2013, KDD Cup '13.

Field-aware Factorization Machines for CTR Prediction

Chih-Jen Lin, Wei-Sheng Chin, Yu-Chin Juan, 2016, RecSys.

A tutorial on n-support vector machines

Bernhard Sch, Pai-Hsuen Chen, Chih-Jen Lin, 2005 .

Incremental and decremental training for linear classification

Cheng-Hao Tsai, Chieh-Yen Lin, Chih-Jen Lin, 2014, KDD.

Parameter Selection for Linear Support Vector Regression

Chih-Jen Lin, Jui-Yang Hsia, Chih-Jen Lin, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Dual coordinate descent methods for logistic regression and maximum entropy models

Chih-Jen Lin, Hsiang-Fu Yu, Fang-Lan Huang, 2011, Machine Learning.

Preconditioned Conjugate Gradient Methods in Truncated Newton Frameworks for Large-scale Linear Classification

Chih-Jen Lin, Chih-Yang Hsia, Wei-Lin Chiang, 2018, ACML.

A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems

Chih-Jen Lin, Wei-Sheng Chin, Yu-Chin Juan, 2015, ACM Trans. Intell. Syst. Technol..

A Bayesian Approximation Method for Online Ranking

Chih-Jen Lin, Ruby C. Weng, Chih-Jen Lin, 2011, J. Mach. Learn. Res..

Ranking individuals by group comparisons

Chih-Jen Lin, Tzu-Kuo Huang, Ruby C. Weng, 2006, ICML.

Combining SVMs with Various Feature Selection Strategies

Chih-Jen Lin, Yi-Wei Chen, Chih-Jen Lin, 2006, Feature Extraction.

A Generalized Bradley-Terry Model: From Group Competition to Individual Skill

Chih-Jen Lin, Tzu-Kuo Huang, Ruby C. Weng, 2004, NIPS.

Improving Ad Click Prediction by Considering Non-displayed Events

Chih-Jen Lin, Hong Zhu, Chih-Yao Chang, 2019, CIKM.

Dual Coordinate-Descent Methods for Linear One-Class SVM and SVDD

Chih-Jen Lin, Hung-Yi Chou, Pin-Yen Lin, 2020, SDM.

Selection of Negative Samples for One-class Matrix Factorization

Chih-Jen Lin, Hsiang-Fu Yu, Mikhail Bilenko, 2017, SDM.

An Unconstrained Convex Programming Approach to Linear Semi-Infinite Programming

Chih-Jen Lin, Shu-Cherng Fang, Soon-Yi Wu, 1998, SIAM J. Optim..

On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization

Chih-Jen Lin, Chih-Jen Lin, 2007, IEEE Transactions on Neural Networks.

A Study on Threshold Selection for Multi-label Classification

Chih-Jen Lin, Rong-En Fan, Chih-Jen Lin, 2007 .

An Efficient Alternating Newton Method for Learning Factorization Machines

Chih-Jen Lin, Wei-Sheng Chin, Bo-Wen Yuan, 2018, ACM Trans. Intell. Syst. Technol..

LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems

Chih-Jen Lin, Wei-Sheng Chin, Yu-Chin Juan, 2016, J. Mach. Learn. Res..

Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments

Chih-Jen Lin, Wei-Lin Chiang, Mu-Chu Lee, 2016, KDD.

A Study on L2-Loss (Squared Hinge-Loss) Multiclass SVM

Chih-Jen Lin, Ching-Pei Lee, Chih-Jen Lin, 2013, Neural Computation.

AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction

Xiuqiang He, Jui-Yang Hsia, Zhenhua Dong, 2020, 2020 21st IEEE International Conference on Mobile Data Management (MDM).

Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation

Shou-De Lin, Chun-Liang Li, Hsuan-Tien Lin, 2012, KDD Cup.

Warm Start for Parameter Selection of Linear Classifiers

Bo-Yu Chu, Chia-Hua Ho, Cheng-Hao Tsai, 2015, KDD.

A Study on Trust Region Update Rules in Newton Methods for Large-scale Linear Classification

Chih-Jen Lin, Ya Zhu, Chih-Yang Hsia, 2017, ACML.

A study on reduced support vector machines

Chih-Jen Lin, Kuan-Ming Lin, Chih-Jen Lin, 2003, IEEE Trans. Neural Networks.

Subsampled Hessian Newton Methods for Supervised Learning

Chih-Jen Lin, Chien-Chih Wang, Chun-Heng Huang, 2015, Neural Computation.

Linear and Kernel Classification: When to Use Which?

Chih-Jen Lin, Hsin-Yuan Huang, Chih-Jen Lin, 2016, SDM.

Solving General Capacity Problem by Relaxed Cutting Plane Approach

Chih-Jen Lin, Shu-Cherng Fang, Soon-Yi Wu, 2001, Ann. Oper. Res..

On the convergence of the decomposition method for support vector machines

Chih-Jen Lin, 2001, IEEE Trans. Neural Networks.

Implementation of an inexact approach to solving linear semi-infinite programming problems

Chih-Jen Lin, Shu-Cherng Fang, Soon-Yi Wu, 1995 .

Analytic Center Based Cutting Plane Method for Linear Semi-Infinite Programming

Chih-Jen Lin, Shu-Cherng Fang, Soon-Yi Wu, 2001 .

Optical Coating Designs Using the Family Competition Evolutionary Algorithm

Cheng-Yan Kao, Jorng-Tzong Horng, Chih-Jen Lin, 2001, Evolutionary Computation.

Parallel Spectral Clustering

Edward Y. Chang, Chih-Jen Lin, Wen-Yen Chen, 2008, ECML/PKDD.

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models

Cho-Jui Hsieh, Chih-Jen Lin, Kai-Wei Chang, 2010, J. Mach. Learn. Res..

Dense Non-rigid Point-Matching Using Random Projections

Chih-Jen Lin, Dennis DeCoste, Raffay Hamid, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Incomplete Cholesky Factorizations with Limited Memory

J. J. Moré, Chih-Jen Lin, Jorge J. Mor, 1999, SIAM Journal on Scientific Computing.

A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information

Chih-Jen Lin, Inderjit S. Dhillon, Hsiang-Fu Yu, 2017, AAAI.

Training nu-support vector regression: theory and algorithms.

Chih-Jen Lin, Chih-Chung Chang, 2002, Neural computation.

Parameter Selection for Linear Support Vector Regression

Chih-Jen Lin, Jui-Yang Hsia, 2020, IEEE Transactions on Neural Networks and Learning Systems.

A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems

Chih-Jen Lin, Marco Sciandrone, Giulio Galvan, 2021, J. Mach. Learn. Res..