Another look at statistical learning theory and regularization
By Vladimir Cherkassky, Yunqian Ma. 2009
53 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Ambra Demontis
- Marco Melis
- Igino Corona
- Konrad Rieck
In Computer Vision and Pattern Recognition
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Ambra Demontis
- Klaus-Dieter Thoben
- Konrad Rieck
- Christopher Irgens
In Control and Systems Engineering
- Battista Biggio
- Fabio Roli
- Klaus-Dieter Thoben
- Konrad Rieck
- Giorgio Giacinto
- Christopher Irgens
- Daniel Weimer
- Ambra Demontis
Citations in the corpus
- Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017, background): “Note that the above formulation is quite general, as it represents different learning algorithms, depending on the chosen regularizer and loss function [16].”
- Machine learning in manufacturing: advantages, challenges, and applications (2016, methodology): “SVM can be understood as a practical methodology of the theoretical framework of STL (Cherkassky & Ma, 2009).”