Charles X. Ling

发表

A New Generalized Error Path Algorithm for Model Selection

Bin Gu, Charles X. Ling, C. Ling, 2015, ICML.

Learning Prosodic Patterns for Mandarin Speech Synthesis

Wen Gao, Charles X. Ling, Tingshao Zhu, 2000, Journal of Intelligent Information Systems.

When Does Cotraining Work in Real Data?

Jun Du, Zhi-Hua Zhou, Charles X. Ling, 2011, IEEE Transactions on Knowledge and Data Engineering.

A New Method to Construct the Non-Dominated Set in Multi-Objective Genetic Algorithms

Jinhua Zheng, Zhongzhi Shi, Charles X. Ling, 2004, Intelligent Information Processing.

Cost-Sensitive Test Strategies

Shichao Zhang, Charles X. Ling, Shengli Sheng, 2006, AAAI.

A decision-tree model of balance scale development

Charles X. Ling, William C. Schmidt, 2004, Machine Learning.

Asking Generalized Queries with Minimum Cost

Jun Du, Charles X. Ling, C. Ling, 2011, PAKDD.

Learning good prototypes for classification using filtering and abstraction of instances

Wai Lam, Charles X. Ling, Chi-Kin Keung, 2002, Pattern Recognit..

Adapting cost-sensitive learning for reject option

Jun Du, Charles X. Ling, Eileen A. Ni, 2010, CIKM.

GlucoGuide: An Intelligent Type-2 Diabetes Solution Using Data Mining and Mobile Computing

Yan Luo, Charles X. Ling, Jody Schuurman, 2014, 2014 IEEE International Conference on Data Mining Workshop.

Semi-supervised Self-training for Sentence Subjectivity Classification

Bin Wang, Charles X. Ling, Harry Zhang, 2008, Canadian Conference on AI.

Pelee: A Real-Time Object Detection System on Mobile Devices

Charles X. Ling, Xiang Li, Robert J. Wang, 2018, NeurIPS.

Few-Shot Abstract Visual Reasoning With Spectral Features pdf

Charles X. Ling, Yining Hu, Tanner Bohn, 2019, ArXiv.

Active learning with direct query construction

Jun Du, Charles X. Ling, C. Ling, 2008, KDD.

"Missing is useful": missing values in cost-sensitive decision trees

Shichao Zhang, Charles X. Ling, Shengli Sheng, 2005, IEEE Transactions on Knowledge and Data Engineering.

Ensemble Learning With Attention-Integrated Convolutional Recurrent Neural Network for Imbalanced Speech Emotion Recognition

Victor S. Sheng, Charles X. Ling, Xusheng Ai, 2020, IEEE Access.

When does Co-training Work in Real Data?

Jun Du, Zhi-Hua Zhou, Charles X. Ling, 2009, PAKDD.

Simple Test Strategies for Cost-Sensitive Decision Trees

Qiang Yang, Charles X. Ling, Shengli Sheng, 2005, ECML.

pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry

Wen Gao, Rong Zeng, Qiang Yang, 2005, Bioinform..

Rank Measures for Ordering

Charles X. Ling, Jin Huang, 2005, PKDD.

Cost-Sensitive Decision Trees with Pre-pruning

Jun Du, Charles X. Ling, Zhihua Cai, 2007, Canadian Conference on AI.

Active Learning with Generalized Queries

Jun Du, Charles X. Ling, C. Ling, 2009, 2009 Ninth IEEE International Conference on Data Mining.

Geometric Properties of Naive Bayes in Nominal Domains

Charles X. Ling, Huajie Zhang, 2001, ECML.

Semi‐Supervised Learning in Reconstructed Manifold Space for 3D Caricature Generation

Wen Gao, Yiqiang Chen, Chunyan Miao, 2009, Comput. Graph. Forum.

Artificial Aging of Faces by Support Vector Machines

Charles X. Ling, Jianning Wang, 2004, Canadian Conference on AI.

Intelligent Protein 3D Structure Retrieval System

Xiang Chen, Wen Gao, Yiqiang Chen, 2003, ISMIS.

Efficient Action Extraction with Many-to-Many Relationship between Actions and Features

Mei Liu, Jianfeng Du, Yong Hu, 2011, LORI.

Predicting software escalations with maximum ROI

Charles X. Ling, Nazim H. Madhavji, Shengli Sheng, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Overfitting and generalization in learning discrete patterns

Charles X. Ling, C. Ling, 1995, Neurocomputing.

of the Nonconscious Acquisition A Symbolic Model

Marin Marinov, Charles X. Ling, C. Ling, 1994 .

Decision trees with minimal costs

Qiang Yang, Shichao Zhang, Charles X. Ling, 2004, ICML.

Cognitive agents and multiagent interaction

Charles X. Ling, Jean-Luc Koning, 2003, Cognitive Systems Research.

Discriminative parameter learning for Bayesian networks

Stan Matwin, Charles X. Ling, Harry Zhang, 2008, ICML '08.

Data Mining for Direct Marketing: Problems and Solutions

Charles X. Ling, Chenghui Li, C. Ling, 1998, KDD.

Mining optimal actions for profitable CRM

Qiang Yang, Charles X. Ling, Jie Cheng, 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

Core set analysis in inconsistent decision tables

Weihua Gui, Xiaoli Wang, Charles X. Ling, 2013, Inf. Sci..

Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization

Hui Li, Wenyin Gong, Charles X. Ling, 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Test-cost sensitive classification on data with missing values

Qiang Yang, Rong Pan, Charles X. Ling, 2006, IEEE Transactions on Knowledge and Data Engineering.

A real-coded biogeography-based optimization with mutation

Hui Li, Wenyin Gong, Charles X. Ling, 2010, Appl. Math. Comput..

Computational Cognitive Modeling, the Source of Power, and Other Related Issues

Charles X. Ling, Ron Sun, 1998, AI Mag..

Inverting Implication with Small Training Sets

Stan Matwin, David W. Aha, Charles X. Ling, 1994, ECML.

Alignment Algorithms for Learning to Read Aloud

Handong Wang, Charles X. Ling, 1997, IJCAI.

Mobile-based food classification for Type-2 Diabetes using nutrient and textual features

Yan Luo, Charles X. Ling, Shuang Ao, 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).

Clustering-Based Matrix Factorization pdf

Charles X. Ling, Nima Mirbakhsh, 2013, ArXiv.

Triply Stochastic Gradients on Multiple Kernel Learning

Bin Gu, Huaimin Wang, Charles X. Ling, 2017, UAI.

Hybrid Cost-Sensitive Decision Tree

Charles X. Ling, Shengli Sheng, C. Ling, 2005, PKDD.

An Improved Learning Algorithm for Augmented Naive Bayes

Charles X. Ling, Huajie Zhang, 2001, PAKDD.

The convergence of linear classifiers on large sparse data

Bin Gu, Huaimin Wang, Charles X. Ling, 2018, Neurocomputing.

A Multi-Objective Genetic Algorithm Based on Density

Jinhua Zheng, Charles X. Ling, Xuyong Li, 2007, ISICA.

Learning Sentence Embeddings for Coherence Modelling and Beyond

Charles X. Ling, Yining Hu, Tanner A. Bohn, 2019, RANLP.

Extracting Actionable Knowledge from Decision Trees

Qiang Yang, Jie Yin, Rong Pan, 2007, IEEE Transactions on Knowledge and Data Engineering.

Thresholding for Making Classifiers Cost-sensitive

Victor S. Sheng, Charles X. Ling, V. Sheng, 2006, AAAI.

Extracting Actionable Knowledge from Decision Trees

Qiang Yang, Jie Yin, Charles X. Ling, 2007 .

The Representational Power of Discrete Bayesian Networks

Charles X. Ling, Huajie Zhang, 2002, J. Mach. Learn. Res..

Proper Model Selection with Significance Test

Stan Matwin, Charles X. Ling, Jin Huang, 2008, ECML/PKDD.

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

Wenyin Gong, Charles X. Ling, Zhihua Cai, 2010, Soft Comput..

Cost-Sensitive Learning

Victor S. Sheng, Charles X. Ling, V. Sheng, 2009, Encyclopedia of Data Warehousing and Mining.

Clustering-based factorized collaborative filtering

Charles X. Ling, Nima Mirbakhsh, C. Ling, 2013, RecSys.

Constructive Inductive Logic Programming

Stan Matwin, Charles X. Ling, Stephane Lapointe, 1993, International Joint Conference on Artificial Intelligence.

Using AUC and accuracy in evaluating learning algorithms

Charles X. Ling, Jin Huang, C. Ling, 2005, IEEE Transactions on Knowledge and Data Engineering.

Computing Optimal Attribute Weight Settings for Nearest Neighbor Algorithms

Charles X. Ling, Hangdong Wang, C. Ling, 1997, Artificial Intelligence Review.

Learning to Control Dynamic Systems with Automatic Quantization

Charles X. Ling, Ralph O. Buchal, 1993, ECML.

Reviewer Recommender of Pull-Requests in GitHub

Gang Yin, Huaimin Wang, Yue Yu, 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.

Inductive Learning from Good Examples

Charles X. Ling, C. Ling, 1991, IJCAI.

Active Learning for Hierarchical Text Classification

Xiao Li, Charles X. Ling, Da Kuang, 2012, PAKDD.

Data Sparseness in Linear SVM

Bin Gu, Huaimin Wang, Charles X. Ling, 2015, IJCAI.

Grapheme Generation in Learning to Read English Words

Charles X. Ling, Bei Zhang, 1998, Canadian Conference on AI.

Numeric mapping and learnability of naive bayes

Charles X. Ling, Harry Zhang, 2003, Appl. Artif. Intell..

Learning with Guaranteed Label Quality

Charles X. Ling, Eileen A. Ni, C. Ling, 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

Foundation of Mining Class-Imbalanced Data

Jun Du, Charles X. Ling, Da Kuang, 2012, PAKDD.

Advanced Data Mining and Applications (ADMA)

Xiang Li, Changjie Tang, Charles X. Ling, 2008, ADMA 2008.

Cost-Sensitive Learning and the Class Imbalance Problem

Victor S. Sheng, Charles X. Ling, V. Sheng, 2008 .

Who Should Review this Pull-Request: Reviewer Recommendation to Expedite Crowd Collaboration

Gang Yin, Huaimin Wang, Yue Yu, 2014, 2014 21st Asia-Pacific Software Engineering Conference.

Object Tracking in Frame-Skipping Video Acquired Using Wireless Consumer Cameras

Anlong Ming, Charles X. Ling, Huadong Ma, 2012 .

Some discussions about MOGAs: individual relations, non-dominated set, and application on automatic negotiation

Jinhua Zheng, Zhongzhi Shi, Charles X. Ling, 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

Comparing naive Bayes, decision trees, and SVM with AUC and accuracy

Jingjing Lu, Charles X. Ling, Jin Huang, 2003, Third IEEE International Conference on Data Mining.

Crafting Your Research Future: A Guide to Successful Master's and Ph.D. Degrees in Science & Engineering

Qiang Yang, Charles X. Ling, Qiang Yang, 2012, Crafting Your Research Future.

Constructing Ensembles for Better Ranking

Charles X. Ling, Jin Huang, 2006, Sixth International Conference on Data Mining (ICDM'06).

Decisive Supervised Learning

Charles X. Ling, Eileen A. Ni, Da Kuang, 2013, PAKDD.

Neural Sentence Location Prediction for Summarization pdf

Charles X. Ling, Tanner A. Bohn, C. Ling, 2018, ArXiv.

Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry

Wen Gao, Rong Zeng, Qiang Yang, 2004, Bioinform..

Asking Generalized Queries to Ambiguous Oracle

Jun Du, Charles X. Ling, C. Ling, 2010, ECML/PKDD.

Partial ensemble classifiers selection for better ranking

Charles X. Ling, Jin Huang, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

A Symbolic Model for Learning the Past-Tenses of English Verbs

Marin Marinov, Charles X. Ling, Steven Cherwenka, 1993, IJCAI.

Learning via Prototype Generation and Filtering

Wai Lam, Charles X. Ling, Chi-Kin Keung, 2001 .

Direct Marketing with Fewer Mistakes

Charles X. Ling, Eileen A. Ni, C. Ling, 2011, ADMA.

Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information

Charles X. Ling, Nima Mirbakhsh, C. Ling, 2015, TKDD.

Co-Training on Handwritten Digit Recognition

Jun Du, Charles X. Ling, 2009, Canadian Conference on AI.

ICDMW 2012 : the 12th IEEE International Conference on Data Mining Workshops, Brussels, Belgium, December 10, 2012

Xindong Wu, Bart Goethals, Jeffrey Xu Yu, 2012 .

Learning Contextual Dependency Network Models for Link-Based Classification

Wen Gao, Qiang Yang, Tiejun Huang, 2006, IEEE Transactions on Knowledge and Data Engineering.

Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1

William Stafford Noble, Charles X. Ling, Qiang Yang, 2005, TCBB.

A Kernel-Based Case Retrieval Algorithm with Application to Bioinformatics

Wen Gao, Rong Zeng, Yiqiang Chen, 2004, PRICAI.

Active Learning with Human-Like Noisy Oracle

Jun Du, Charles X. Ling, C. Ling, 2010, 2010 IEEE International Conference on Data Mining.

A Decision-Tree Model of Balance Scale Development

Charles X. Ling, William C. Schmidt, 2004, Machine Learning.

Introduction to special issue on machine learning for business applications

Charles X. Ling, 2011, TIST.

Learnability of Augmented Naive Bayes in Nonimal Domains

Charles X. Ling, Huajie Zhang, 2001, ICML.

Extracting Academic Activity Transaction in Chinese Documents

Charles X. Ling, Fang Huang, Shanmei Tang, 2014 .

A Unified Framework for Lifelong Learning in Deep Neural Networks pdf

Charles X. Ling, Tanner Bohn, 2019, ArXiv.

Mining web logs for actionable knowledge

Jianfeng Gao, Charles X. Ling, Qiang Yang, 2004 .

A New Search Engine Integrating Hierarchical Browsing and Keyword Search

Xiao Li, Charles X. Ling, Da Kuang, 2011, IJCAI.

Improving Encarta Search Engine Performance by Mining User Logs

Jianfeng Gao, Charles X. Ling, HongJiang Zhang, 2002, Int. J. Pattern Recognit. Artif. Intell..

Toward Bayesian Classifiers with Accurate Probabilities

Charles X. Ling, Huajie Zhang, 2002, PAKDD.

A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution

Wenyin Gong, Charles X. Ling, Zhihua Cai, 2009, ISICA.

The Convergence Behavior of Naive Bayes on Large Sparse Datasets

Huaimin Wang, Charles X. Ling, Xiang Li, 2015, 2015 IEEE International Conference on Data Mining.

A Multi-objective Genetic Algorithm Based on Quick Sort

Jinhua Zheng, Zhongzhi Shi, Charles X. Ling, 2004, Canadian Conference on AI.

Test-cost sensitive naive Bayes classification

Qiang Yang, Charles X. Ling, Lin Deng, 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

A clustering-based differential evolution for global optimization

Wenyin Gong, Charles X. Ling, Zhihua Cai, 2011, Appl. Soft Comput..

Fast Generalized Distillation for Semi-Supervised Domain Adaptation

Charles X. Ling, Xiang Li, Shuang Ao, 2017, AAAI.

Self-Directed Learner

Charles X. Ling, Eileen A. Ni, C. Ling, 2010, 2010 International Conference on Artificial Intelligence and Education (ICAIE).

Maximum profit mining and its application in software development

Victor S. Sheng, Charles X. Ling, Nazim H. Madhavji, 2006, KDD '06.

A Fundamental Issue of Naive Bayes

Charles X. Ling, Harry Zhang, 2003, Canadian Conference on AI.

Catching Attention with Automatic Pull Quote Selection

Charles X. Ling, Tanner Bohn, 2020, COLING.

Asking Generalized Queries to Domain Experts to Improve Learning

Jun Du, Charles X. Ling, C. Ling, 2010, IEEE Transactions on Knowledge and Data Engineering.

Editorial Notes

Nick Cercone, Charles X. Ling, 2002, Int. J. Found. Comput. Sci..

Learning Recursive Relations with Randomly Selected Small Training Sets

Stan Matwin, David W. Aha, Charles X. Ling, 1994, ICML.

Hongyan Liu, Jun He, Xiaoyong Du, 2013, IEEE Transactions on Knowledge and Data Engineering.

ODE: A Fast and Robust Differential Evolution Based on Orthogonal Design

Wenyin Gong, Charles X. Ling, Zhihua Cai, 2006, Australian Conference on Artificial Intelligence.

IndexToolkit: an open source toolbox to index protein databases for high-throughput proteomics

Wen Gao, Ruixiang Sun, Dequan Li, 2006, Bioinform..

Active Teaching for Inductive Learners

Jun Du, Charles X. Ling, C. Ling, 2011, SDM.

A Symbolic Model of the Nonconscious Acquisition of Information

Marin Marinov, Charles X. Ling, C. Ling, 1994, Cogn. Sci..

Setting Attribute Weights for Nearest Neighour Learning Algorithms Using C4.5

Charles X. Ling, John J. Parry, Hangdong Wang, 1997, Int. J. Pattern Recognit. Artif. Intell..

Advances in Knowledge Discovery and Data Mining

Charles X. Ling, 2012, Lecture Notes in Computer Science.

Keyphrase Extraction Using Semantic Networks Structure Analysis

Zhi Zhou, Tiejun Huang, Yonghong Tian, 2006, Sixth International Conference on Data Mining (ICDM'06).

Discovering Classification from Data of Multiple Sources

Qiang Yang, Charles X. Ling, Qiang Yang, 2006, Data Mining and Knowledge Discovery.

Learning Full Pitch Variation Patterns with NeuralNets

Wen Gao, Charles X. Ling, Tingshao Zhu, 1999 .

Active Learning with c-Certainty

Charles X. Ling, Eileen A. Ni, C. Ling, 2012, PAKDD.

Region-Based Global Reasoning Networks

Charles X. Ling, Huadong Ma, Peilun Du, 2020, AAAI.

Test strategies for cost-sensitive decision trees

Qiang Yang, Victor S. Sheng, Charles X. Ling, 2006, IEEE Transactions on Knowledge and Data Engineering.

Learning Classifications from Multiple Sources of Unsupervised Data

Handong Wang, Charles X. Ling, 1996, Canadian Conference on AI.

Adapting New Categories for Food Recognition with Deep Representation

Charles X. Ling, Shuang Ao, 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

AUC: A Better Measure than Accuracy in Comparing Learning Algorithms

Charles X. Ling, Jin Huang, Harry Zhang, 2003, Canadian Conference on AI.

Leveraging clustering to improve collaborative filtering

Charles X. Ling, Nima Mirbakhsh, 2018, Inf. Syst. Frontiers.

An optimal model with a lower bound of recall for imbalanced speech emotion recognition

Victor S. Sheng, Charles X. Ling, Wei Fang, 2020, Multimedia Tools and Applications.

A Reliable People Counting System via Multiple Cameras

Charles X. Ling, Huadong Ma, Chengbin Zeng, 2012, TIST.

Feature value acquisition in testing: a sequential batch test algorithm

Victor S. Sheng, Charles X. Ling, V. Sheng, 2006, ICML.

The Learnability of Naive Bayes

Charles X. Ling, Huajie Zhang, Zhiduo Zhao, 2000, Canadian Conference on AI.

Hybrid differential evolution based on fuzzy C-means clustering

Jun Du, Wenyin Gong, Charles X. Ling, 2009, GECCO '09.

Roulette Sampling for Cost-Sensitive Learning

Victor S. Sheng, Charles X. Ling, V. Sheng, 2007, ECML.

Customized classification learning based on query projections

Wai Lam, Charles X. Ling, Yiqiu Han, 2007, Inf. Sci..

Class Imbalance Problem

Victor S. Sheng, Charles X. Ling, V. Sheng, 2010, Encyclopedia of Machine Learning.

Partial example acquisition in cost-sensitive learning

Victor S. Sheng, Charles X. Ling, 2007, KDD '07.

Tag recommendation for open source software

Gang Yin, Xiao Li, Huaimin Wang, 2013, Frontiers of Computer Science.

A Conceptual Framework for Lifelong Learning

Charles X. Ling, Tanner A. Bohn, Tanner Bohn, 2019 .

Class Imbalance Problem

Victor S. Sheng, Charles X. Ling, V. Sheng, 2010, Encyclopedia of Machine Learning.

Cost-Sensitive Learning

Victor S. Sheng, Charles X. Ling, V. Sheng, 2009, Encyclopedia of Data Warehousing and Mining.

Effective Top-Down Active Learning for Hierarchical Text Classification

Xiao Li, Huaimin Wang, Charles X. Ling, 2013, PAKDD.

Learning to Control Dynamic Systems with Automatic Quantization

Charles X. Ling, Ralph O. Buchal, C. Ling, 1993, Adapt. Behav..

Effective Multiclass Transfer for Hypothesis Transfer Learning

Charles X. Ling, Xiang Li, Shuang Ao, 2017, PAKDD.

Cost-Sensitive Learning

Victor S. Sheng, Charles X. Ling, V. Sheng, 2009, Encyclopedia of Data Warehousing and Mining.

Detecting both superimposed and scene text with multiple languages and multiple alignments in video

Charles X. Ling, Huadong Ma, Xiaodong Huang, 2012, Multimedia Tools and Applications.

Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics - Part 1

Qiang Yang, William Stafford Noble, Charles X. Ling, 2005, IEEE ACM Trans. Comput. Biol. Bioinform..

Mining Software Profile across Multiple Repositories for Hierarchical Categorization

Gang Yin, Huaimin Wang, Tao Wang, 2013, 2013 IEEE International Conference on Software Maintenance.

Representational Upper Bounds of Bayesian Networks

Charles X. Ling, Huajie Zhang, 2002, ICML.

1 Learning good prototypes for classication usingltering and abstraction of instances

Wai Lam, Charles X. Ling, Chi-Kin Keung, 2001 .

Supervised Learning with Minimal Effort

Charles X. Ling, Eileen A. Ni, C. Ling, 2010, PAKDD.

Guest Editor's Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 2

Qiang Yang, William Stafford Noble, Charles X. Ling, 2005, TCBB.

Dynamic Ensemble Re-Construction for Better Ranking

Charles X. Ling, Jin Huang, 2005, PKDD.

Mining generalized query patterns from web logs

Jianfeng Gao, Charles X. Ling, HongJiang Zhang, 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models

Charles X. Ling, C. Ling, 1993, J. Artif. Intell. Res..

Constructing New and Better Evaluation Measures for Machine Learning

Charles X. Ling, Jin Huang, C. Ling, 2007, IJCAI.

Software Escalation Prediction with Data Mining

Charles X. Ling, Nazim H. Madhavji, Shengli Sheng, 2004 .

Proceedings of the 12th IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, 2012

Geoffrey I. Webb, Bart Goethals, Jeffrey Xu Yu, 2012 .

Tackling Non-forgetting and Forward Transfer with a Unified Lifelong Learning Approach

Charles X. Ling, Tanner A. Bohn, Xinyu Yun, 2020 .

Postprocessing decision trees to extract actionable knowledge

Qiang Yang, Jie Yin, Charles X. Ling, 2003, Third IEEE International Conference on Data Mining.

A Deep Learning Framework for Lifelong Machine Learning pdf

Charles X. Ling, Tanner Bohn, 2021, ArXiv.

Introducing new predicates to model scientific revolution

Charles X. Ling, C. Ling, 1995 .

Learning Singly-Recursive Relations from Small Datasets 1

Stan Matwin, David W. Aha, Charles X. Ling, 1993 .

Evaluating Model Selection Abilities of Performance Measures

Charles X. Ling, Jin Huang, 2006 .