DagRep.5.8.54.pdf
- Filesize: 0.86 MB
- 20 pages
Document
Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361)
Authors and all authors of the abstracts in this report
-
Part of:
Issue:
Dagstuhl Reports, Volume 5, Issue 8
Volume: Dagstuhl Reports, Volume 5
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2016-01-15
Cite AsGet BibTex
Matthias Hein, Gabor Lugosi, and Lorenzo Rosasco. Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361). In Dagstuhl Reports, Volume 5, Issue 8, pp. 54-73, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)
https://doi.org/10.4230/DagRep.5.8.54
https://doi.org/10.4230/DagRep.5.8.54
Abstract
Machine learning has become a core field in computer science. Over
the last decade the statistical machine learning approach has been successfully applied in many areas such as bioinformatics, computer vision, robotics and information retrieval. The main reasons for the success of machine learning are its strong theoretical foundations and its
multidisciplinary approach integrating aspects of computer science, applied mathematics, and statistics among others. The goal of the seminar was to bring together again experts from computer science, mathematics and statistics to discuss the state of the art in machine learning and identify and formulate the key challenges in learning which have to be addressed in the future.
The main topics of this seminar were:
- Interplay between Optimization and Learning,
- Learning Data Representations.
Keywords
- learning theory
- non-smooth optimization (convex and non-convex)
- signal processing
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads