Examples of Learning Curves from a Modified VC-Formalism
Abstract
We examine the issue of evaluation of model specific parameters in a modified VC-formalism. Two examples are analyzed: the 2-dimensional homogeneous perceptron and the I-dimensional higher order neuron. Both models are solved theoretically, and their learning curves are com(cid:173) pared against true learning curves. It is shown that the formalism has the potential to generate a variety of learning curves, including ones displaying ''phase transitions."
Cite
Text
Kowalczyk et al. "Examples of Learning Curves from a Modified VC-Formalism." Neural Information Processing Systems, 1995.Markdown
[Kowalczyk et al. "Examples of Learning Curves from a Modified VC-Formalism." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/kowalczyk1995neurips-examples/)BibTeX
@inproceedings{kowalczyk1995neurips-examples,
title = {{Examples of Learning Curves from a Modified VC-Formalism}},
author = {Kowalczyk, Adam and Szymanski, Jacek and Bartlett, Peter L. and Williamson, Robert C.},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {344-350},
url = {https://mlanthology.org/neurips/1995/kowalczyk1995neurips-examples/}
}