Stochastic Learning

This paper summarizes my lecture at the Machine Learning Summer School 2003, Tübingen.

Abstract: This contribution presents an overview of the theoretical and practical aspects of the broad family of learning algorithms based on Stochastic Gradient Descent, including Perceptrons, Adalines, K-Means, LVQ, Multi-Layer Networks, and Graph Transformer Networks.

Léon Bottou: Stochastic Learning, Advanced Lectures on Machine Learning, 146-168, Edited by Olivier Bousquet and Ulrike von Luxburg, Lecture Notes in Artificial Intelligence, LNAI 3176, Springer Verlag, Berlin, 2004.

mlss-2003.djvu mlss-2003.pdf mlss-2003.ps.gz

@incollection{bottou-mlss-2004,
  author = {Bottou, L\'{e}on},
  title = {Stochastic Learning},
  booktitle = {Advanced Lectures on Machine Learning},
  pages = {146-168},
  publisher = {Springer Verlag},
  year = {2004},
  editor = {Bousquet, Olivier and von Luxburg, Ulrike},
  series = {Lecture Notes in Artificial Intelligence, LNAI~3176},
  address = {Berlin},
  url = {http://leon.bottou.org/papers/bottou-mlss-2004},
}
papers/bottou-mlss-2004.txt · Last modified: 2008/08/18 14:59 by leonb
www.chimeric.de Valid CSS Driven by DokuWiki do yourself a favour and use a real browser - get firefox!! Recent changes RSS feed Valid XHTML 1.0