Min-Ling Zhang

According to our database1, Min-Ling Zhang authored at least 65 papers between 2003 and 2020.

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2020
Large-scale multi-label classification using unknown streaming images.
Pattern Recognit., 2020

Multi-dimensional classification via <i>k</i>NN feature augmentation.
Pattern Recognit., 2020

Preface.
J. Comput. Sci. Technol., 2020

Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Maximum Margin Multi-Dimensional Classification.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Neighborhood kinship preserving hashing for supervised learning.
Signal Process. Image Commun., 2019

Supervised representation learning for multi-label classification.
Mach. Learn., 2019

Transfer synthetic over-sampling for class-imbalance learning with limited minority class data.
Frontiers Comput. Sci., 2019

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Adaptive Graph Guided Disambiguation for Partial Label Learning.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Multi-View Multi-Label Learning with View-Specific Information Extraction.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Multi-Label Learning with Regularization Enriched Label-Specific Features.
Proceedings of The 11th Asian Conference on Machine Learning, 2019

CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Multi-Dimensional Classification via kNN Feature Augmentation.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Partial Multi-Label Learning via Credible Label Elicitation.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Weakly Supervised POS Tagging without Disambiguation.
ACM Trans. Asian Low Resour. Lang. Inf. Process., 2018

Binary relevance for multi-label learning: an overview.
Frontiers Comput. Sci., 2018

Towards Mitigating the Class-Imbalance Problem for Partial Label Learning.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Towards Enabling Binary Decomposition for Partial Label Learning.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Imbalanced Augmented Class Learning with Unlabeled Data by Label Confidence Propagation.
Proceedings of the IEEE International Conference on Data Mining, 2018

A new R2 indicator for better hypervolume approximation.
Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Feature-Induced Labeling Information Enrichment for Multi-Label Learning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Multi-label Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Disambiguation-Free Partial Label Learning.
IEEE Trans. Knowl. Data Eng., 2017

Maximum margin partial label learning.
Mach. Learn., 2017

Inductive Semi-supervised Multi-Label Learning with Co-Training.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Binary Linear Compression for Multi-label Classification.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Multi-label Learning with Label-Specific Features via Clustering Ensemble.
Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

Confidence-Rated Discriminative Partial Label Learning.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Partial Label Learning via Feature-Aware Disambiguation.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Multi-Label Manifold Learning.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Lift: Multi-Label Learning with Label-Specific Features.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Solving the Partial Label Learning Problem: An Instance-Based Approach.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Towards Class-Imbalance Aware Multi-Label Learning.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-label Learning.
Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

2014
A Review on Multi-Label Learning Algorithms.
IEEE Trans. Knowl. Data Eng., 2014

Disambiguation-Free Partial Label Learning.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Enhancing Binary Relevance for Multi-label Learning with Controlled Label Correlations Exploitation.
Proceedings of the PRICAI 2014: Trends in Artificial Intelligence, 2014

2013
Exploiting unlabeled data to enhance ensemble diversity.
Data Min. Knowl. Discov., 2013

Multi-Label Classification with Unlabeled Data: An Inductive Approach.
Proceedings of the Asian Conference on Machine Learning, 2013

2012
Introduction to the special issue on learning from multi-label data.
Mach. Learn., 2012

Multi-instance multi-label learning.
Artif. Intell., 2012

2011
CoTrade: Confident Co-Training With Data Editing.
IEEE Trans. Syst. Man Cybern. Part B, 2011

LIFT: Multi-Label Learning with Label-Specific Features.
Proceedings of the IJCAI 2011, 2011

2010
Multi-label learning by exploiting label dependency.
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm.
Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010

2009
Ml-rbf : RBF Neural Networks for Multi-Label Learning.
Neural Process. Lett., 2009

Feature selection for multi-label naive Bayes classification.
Inf. Sci., 2009

MIMLRBF: RBF neural networks for multi-instance multi-label learning.
Neurocomputing, 2009

Classifier Ensemble with Unlabeled Data
CoRR, 2009

Multi-instance clustering with applications to multi-instance prediction.
Appl. Intell., 2009

2008
MIML: A Framework for Learning with Ambiguous Objects
CoRR, 2008

M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning.
Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

2007
ML-KNN: A lazy learning approach to multi-label learning.
Pattern Recognit., 2007

Solving multi-instance problems with classifier ensemble based on constructive clustering.
Knowl. Inf. Syst., 2007

Multi-Label Learning by Instance Differentiation.
Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006
Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization.
IEEE Trans. Knowl. Data Eng., 2006

Adapting RBF Neural Networks to Multi-Instance Learning.
Neural Process. Lett., 2006

Multi-Instance Multi-Label Learning with Application to Scene Classification.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005
A k-nearest neighbor based algorithm for multi-label classification.
Proceedings of the 2005 IEEE International Conference on Granular Computing, 2005

2004
Improve Multi-Instance Neural Networks through Feature Selection.
Neural Process. Lett., 2004

Ensembles of Multi-Instance Neural Networks.
Proceedings of the Intelligent Information Processing II, 2004

2003
A Novel Bag Generator for Image Database Retrieval With Multi-Instance Learning Techniques.
Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2003), 2003

Ensembles of Multi-instance Learners.
Proceedings of the Machine Learning: ECML 2003, 2003


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