Wiwatcharakoses C. and Berrar D. (2019) . SOINN+, a self-organizing incremental neural network for unsupervised learning from noisy data streams. Expert Systems with Applications. [link]

Wiwatcharakoses C. and Berrar D. (2019) Self-organizing incremental neural networks for continual learning. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China, 2019, pp. 6476-6477.

Prof. Mohamed Nadif from Paris Descartes University will visit us and deliver a talk titled "Co-clustering: Models and Algorithms".

Summary:

Discovering the inherent groupings in large quantities of data, represented by a data matrix, is one of the major challenges of machine learning. Classical clustering procedures seek to construct separately an optimal partition of rows (individuals, instances) or, sometimes, of columns (attributes, variables). In contrast, co-clustering methods cluster the rows and the columns simultaneously and organize the data into homogeneous blocks (after suitable permutations). Methods of this kind have practical importance in a wide variety of applications, such as document clustering, bioinformatics, and collaborative filtering. This presentation will give a brief survey of co-clustering under different approaches, with a focus on document clustering.

For: All faculty and students of Tokyo Tech (no registration required)

Where: Tokyo Institute of Technology, Ookayama Campus, West Building W8, 10th floor, room W8E1001

When: 24 April 2019, 14:00–15:00 (talk with Q&A)

Call for papers for International Joint Conference on Artificial Intelligence, Macao, China, 10-16 August 2019.

Daniel will be serving as senior PC member.

Berrar D., Lopes P., Davis J., and Dubitzky W. (2018) Guest editorial: special issue on machine learning for soccer. Machine Learning, https://doi.org/10.1007/s10994-018-5763-8.

Ranganathan S., Nakai K., and Schonbach C. (eds.) (2018) Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. 2500 pages, Elsevier, ISBN: 9780128114148.

Chapters by Berrar D.:

Introduction to the non-parametric bootstrap [pdf preprint]

Cross-validation

Performance measures for binary classification

Bayes' theorem and naive Bayes classifier [pdf preprint]

Soccer is the biggest global sport and a fast-growing, multi-billion dollar industry. Advanced data analytics are being more frequently employed on both the club and national levels to improve performance, equipment, marketing, scouting, etc. Soccer therefore offers interesting challenges for the machine learning community. This special issue solicited articles on all aspects of data analysis and machine learning for soccer. As part of the special issue, we posed the 2017 Soccer Prediction Challenge that revolved around predicting the outcomes of future soccer matches. This special issue features selected papers of the top-performing teams that participated in the Challenge.