Chih-Jen Lin

IEEE Trans. Neural Networks Learn. Syst., 2020

Introduction to the Special Issue on the Best Papers from KDD 2018.

[BibT_eX]

[DOI]

Hui Xiong

ACM Trans. Knowl. Discov. Data, 2020

Newton Methods for Convolutional Neural Networks.

[BibT_eX]

[DOI]

Chien-Chih Wang

Kent Loong Tan

ACM Trans. Intell. Syst. Technol., 2020

An Efficient Newton Method for Extreme Similarity Learning with Nonlinear Embeddings.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

Hung-Yi Chou

Pin-Yen Lin

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Two-variable Dual Coordinate Descent Methods for Linear SVM with/without the Bias Term.

[BibT_eX]

[DOI]

Chi-Cheng Chiu

Pin-Yen Lin

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Unbiased Ad Click Prediction for Position-aware Advertising Systems.

[BibT_eX]

[DOI]

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction.

[BibT_eX]

[DOI]

Proceedings of the 21st IEEE International Conference on Mobile Data Management, 2020

2019

The Common-directions Method for Regularized Empirical Risk Minimization.

[BibT_eX]

[DOI]

Po-Wei Wang

J. Mach. Learn. Res., 2019

Improving Ad Click Prediction by Considering Non-displayed Events.

[BibT_eX]

[DOI]

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

2018

An Efficient Alternating Newton Method for Learning Factorization Machines.

[BibT_eX]

[DOI]

ACM Trans. Intell. Syst. Technol., 2018

Distributed Newton Methods for Deep Neural Networks.

[BibT_eX]

[DOI]

Neural Comput., 2018

Limited-memory Common-directions Method for Distributed Ll-regularized Linear Classification.

[BibT_eX]

[DOI]

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Naive Parallelization of Coordinate Descent Methods and an Application on Multi-core L1-regularized Classification.

[BibT_eX]

[DOI]

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

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

[BibT_eX]

[DOI]

Chih-Yang Hsia

Wei-Lin Chiang

Proceedings of The 10th Asian Conference on Machine Learning, 2018

2017

Selection of Negative Samples for One-class Matrix Factorization.

[BibT_eX]

[DOI]

Hsiang-Fu Yu

Mikhail Bilenko

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization.

[BibT_eX]

[DOI]

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

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

[BibT_eX]

[DOI]

Chih-Yang Hsia

Ya Zhu

Proceedings of The 9th Asian Conference on Machine Learning, 2017

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

[BibT_eX]

[DOI]

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

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

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2016

Linear and Kernel Classification: When to Use Which?

[BibT_eX]

[DOI]

Hsin-Yuan Huang

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Field-aware Factorization Machines for CTR Prediction.

[BibT_eX]

[DOI]

Proceedings of the 10th ACM Conference on Recommender Systems, 2016

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

[BibT_eX]

[DOI]

Wei-Lin Chiang

Mu-Chu Lee

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015

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

[BibT_eX]

[DOI]

ACM Trans. Intell. Syst. Technol., 2015

Subsampled Hessian Newton Methods for Supervised Learning.

[BibT_eX]

[DOI]

Chien-Chih Wang

Chun-Heng Huang

Neural Comput., 2015

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

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2015

Distributed Newton Methods for Regularized Logistic Regression.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

Warm Start for Parameter Selection of Linear Classifiers.

[BibT_eX]

[DOI]

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

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

[BibT_eX]

[DOI]

Mu-Chu Lee

Wei-Lin Chiang

Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

2014

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

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2014

Large-Scale Linear RankSVM.

[BibT_eX]

[DOI]

Neural Comput., 2014

Iteration complexity of feasible descent methods for convex optimization.

[BibT_eX]

[DOI]

Po-Wei Wang

J. Mach. Learn. Res., 2014

Effective string processing and matching for author disambiguation.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2014

Large-scale Kernel RankSVM.

[BibT_eX]

[DOI]

Tzu-Ming Kuo

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Incremental and decremental training for linear classification.

[BibT_eX]

[DOI]

Cheng-Hao Tsai

Chieh-Yen Lin

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

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

[BibT_eX]

[DOI]

Proceedings of the 2014 IEEE International Conference on Big Data (IEEE BigData 2014), 2014

Support Vector Machines.

[BibT_eX]

[DOI]

Po-Wei Wang

Proceedings of the Data Classification: Algorithms and Applications, 2014

2013

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

[BibT_eX]

[DOI]

Neural Comput., 2013

A fast parallel SGD for matrix factorization in shared memory systems.

[BibT_eX]

[DOI]

Proceedings of the Seventh ACM Conference on Recommender Systems, 2013

Large-Scale Video Summarization Using Web-Image Priors.

[BibT_eX]

[DOI]

Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

Dense Non-rigid Point-Matching Using Random Projections.

[BibT_eX]

[DOI]

Raffay Hamid

Dennis DeCoste

Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012

Large Linear Classification When Data Cannot Fit in Memory.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2012

Recent Advances of Large-Scale Linear Classification.

[BibT_eX]

[DOI]

Guo-Xun Yuan

Proc. IEEE, 2012

An Improved GLMNET for L1-regularized Logistic Regression.

[BibT_eX]

[DOI]

Guo-Xun Yuan

J. Mach. Learn. Res., 2012

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

[BibT_eX]

[DOI]

Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011, 2012

Large-scale linear support vector regression.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2012

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

[BibT_eX]

[DOI]

Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011, 2012

Experiences and lessons in developing industry-strength machine learning and data mining software.

[BibT_eX]

[DOI]

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

2011

LIBSVM: A library for support vector machines.

[BibT_eX]

[DOI]

Chih-Chung Chang

ACM Trans. Intell. Syst. Technol., 2011

Parallel Spectral Clustering in Distributed Systems.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2011

Dual coordinate descent methods for logistic regression and maximum entropy models.

[BibT_eX]

[DOI]

Hsiang-Fu Yu

Fang-Lan Huang

Mach. Learn., 2011

A Bayesian Approximation Method for Online Ranking.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2011

2010

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

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2010

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2010

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

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2010

Active learning strategies using SVMs.

[BibT_eX]

[DOI]

Ming-Hen Tsai

Proceedings of the International Joint Conference on Neural Networks, 2010

Designing, Analyzing and Exploiting Stake-Based Social Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2010

2009

An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes.

[BibT_eX]

[DOI]

Proceedings of KDD-Cup 2009 competition, Paris, France, June 28, 2009, 2009

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy.

[BibT_eX]

[DOI]

Proceedings of the ACL 2009, 2009

2008

Trust Region Newton Method for Logistic Regression.

[BibT_eX]

[DOI]

S. Sathiya Keerthi

J. Mach. Learn. Res., 2008

LIBLINEAR: A Library for Large Linear Classification.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2008

Feature Ranking Using Linear SVM.

[BibT_eX]

[DOI]

Yin-Wen Chang

Proceedings of the Causation and Prediction Challenge at WCCI 2008, 2008

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

[BibT_eX]

[DOI]

Kai-Wei Chang

Cho-Jui Hsieh

J. Mach. Learn. Res., 2008

Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods.

[BibT_eX]

[DOI]

J. Biomed. Informatics, 2008

Parallel Spectral Clustering.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

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

[BibT_eX]

[DOI]

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

A dual coordinate descent method for large-scale linear SVM.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning, 2008

2007

On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks, 2007

Projected Gradient Methods for Nonnegative Matrix Factorization.

[BibT_eX]

[DOI]

Neural Comput., 2007

A note on Platt's probabilistic outputs for support vector machines.

[BibT_eX]

[DOI]

Hsuan-Tien Lin

Mach. Learn., 2007

The NTU Toolkit and Framework for High-Level Feature Detection at TRECVID 2007.

[BibT_eX]

[DOI]

Proceedings of the TRECVID 2007 workshop participants notebook papers, 2007

Trust region Newton methods for large-scale logistic regression.

[BibT_eX]

[DOI]

S. Sathiya Keerthi

Proceedings of the Machine Learning, 2007

2006

A study on SMO-type decomposition methods for support vector machines.

[BibT_eX]

[DOI]

Pai-Hsuen Chen

Rong-En Fan

IEEE Trans. Neural Networks, 2006

Generalized Bradley-Terry Models and Multi-Class Probability Estimates.

[BibT_eX]

[DOI]

Tzu-Kuo Huang

J. Mach. Learn. Res., 2006

Ranking individuals by group comparisons.

[BibT_eX]

[DOI]

Tzu-Kuo Huang

Proceedings of the Machine Learning, 2006

Combining SVMs with Various Feature Selection Strategies.

[BibT_eX]

[DOI]

Yi-Wei Chen

Proceedings of the Feature Extraction - Foundations and Applications, 2006

2005

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

[BibT_eX]

[DOI]

Ming-Wei Chang

Neural Comput., 2005

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

[BibT_eX]

[DOI]

Rong-En Fan

Pai-Hsuen Chen

J. Mach. Learn. Res., 2005

Training Support Vector Machines via SMO-Type Decomposition Methods.

[BibT_eX]

[DOI]

Pai-Hsuen Chen

Rong-En Fan

Proceedings of the Algorithmic Learning Theory, 16th International Conference, 2005

2004

Analysis of switching dynamics with competing support vector machines.

[BibT_eX]

[DOI]

Ming-Wei Chang

Ruby Chiu-Hsing Weng

IEEE Trans. Neural Networks, 2004

Decomposition Methods for Linear Support Vector Machines.

[BibT_eX]

[DOI]

Neural Comput., 2004

Probability Estimates for Multi-class Classification by Pairwise Coupling.

[BibT_eX]

[DOI]

Ting-Fan Wu

J. Mach. Learn. Res., 2004

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

[BibT_eX]

[DOI]

Tzu-Kuo Huang

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

A study on reduced support vector machines.

[BibT_eX]

[DOI]

Kuan-Ming Lin

IEEE Trans. Neural Networks, 2003

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel.

[BibT_eX]

[DOI]

S. Sathiya Keerthi

Neural Comput., 2003

Radius Margin Bounds for Support Vector Machines with the RBF Kernel.

[BibT_eX]

[DOI]

Neural Comput., 2003

Special issue on support vector machines.

[BibT_eX]

[DOI]

Neurocomputing, 2003

Decomposition methods for linear support vector machines.

[BibT_eX]

[DOI]

Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

2002

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

[BibT_eX]

[DOI]