Shawe-Taylor, John

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Cristianini, N., & Shawe-Taylor, J. (2000) · Cambridge University Press eBooks

Machine Learning and AlgorithmsMachine Learning and ELM

Cited by 13,855

Estimating the Support of a High-Dimensional Distribution

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation

Machine Learning and AlgorithmsNeural Networks and Applications

Cited by 6,168

Challenges in Representation Learning: A Report on Three Machine Learning Contests

Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al. (2013) · Lecture Notes in Computer Science

Domain Adaptation and Few-Shot LearningMachine Learning and Data Classification

Cited by 1,719

The 2005 PASCAL Visual Object Classes Challenge

Everingham, M., Zisserman, A., Williams, C. K. I., Van Gool, L., Allan, M., Bishop, C. M., Chapelle, O., Dalal, N., Deselaers, T., Dorkó, G., et al. (2006) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 765

Challenges in representation learning: A report on three machine learning contests

Goodfellow, I. J., Erhan, D., Luc Carrier, P., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al. (2015) · Neural Networks

Domain Adaptation and Few-Shot LearningMachine Learning and Data Classification

Cited by 697

Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools

Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024) · Sustainability

Artificial Intelligence in EducationEthics and Social Impacts of AI

Cited by 269

Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

Mourão-Miranda, J., Hardoon, D. R., Hahn, T., Marquand, A. F., Williams, S. C. R., Shawe-Taylor, J., & Brammer, M. (2011) · NeuroImage

Anomaly Detection Techniques and ApplicationsFunctional Brain Connectivity Studies

Cited by 131

Human-AI coevolution

Pedreschi, D., Pappalardo, L., Ferragina, E., Baeza-Yates, R., Barabási, A.-L., Dignum, F., Dignum, V., Eliassi-Rad, T., Giannotti, F., Kertész, J., et al. (2025) · Artificial Intelligence

Ethics and Social Impacts of AIRecommender Systems and Techniques

Cited by 102

Combining Heterogeneous Data Sources for Neuroimaging Based Diagnosis: Re-Weighting and Selecting What Is Important

Donini, M., Monteiro, J. M., Pontil, M., Hahn, T., Fallgatter, A. J., Shawe-Taylor, J., & Mourão-Miranda, J. (2019) · NeuroImage

Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity Studies

Cited by 19

Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning

Yamada, J., Shawe-Taylor, J., & Fountas, Z. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN)

Mathematical and Theoretical Epidemiology and Ecology ModelsReinforcement Learning in Robotics

Cited by 11

Improving Active Learning in Systematic Reviews

Singh, G., Thomas, J., & Shawe-Taylor, J. (2018) · arXiv

Meta-analysis and systematic reviewsInformation Retrieval and Search Behavior

Cited by 11

A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation.

Singh, G., Marshall, I. J., Thomas, J., Shawe-Taylor, J., & Wallace, B. C. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 11

Constructing Artificial Data for Fine-Tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation

Singh, G., Sabet, Z., Shawe-Taylor, J., & Thomas, J. (2021) · Studies in Computational Intelligence

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 7