The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
Abstract
In this work, we introduce a mathematical framework, called the Maximum Cosine Framework or MCF, for deriving new linear classifiers. The method is based on selecting an appropriate bound on the cosine of the angle between the target function and the algorithm’s. To justify its correctness, we use the MCF to show how to regenerate the update rule of Aggressive ROMMA [5]. Moreover, we construct a cosine bound from which we build the Maximum Cosine Perceptron algorithm or, for short, the MCP algorithm. We prove that the MCP shares the same mistake bound like the Perceptron [6]. In addition, we demonstrate the promising performance of the MCP on a real dataset. Our experiments show that, under the restriction of single pass learning, the MCP algorithm outperforms PA [1] and Aggressive ROMMA.
Cite
Text
Bshouty and Haddad-Zaknoon. "The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers." International Conference on Algorithmic Learning Theory, 2016. doi:10.1007/978-3-319-46379-7_14Markdown
[Bshouty and Haddad-Zaknoon. "The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers." International Conference on Algorithmic Learning Theory, 2016.](https://mlanthology.org/alt/2016/bshouty2016alt-maximum/) doi:10.1007/978-3-319-46379-7_14BibTeX
@inproceedings{bshouty2016alt-maximum,
title = {{The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers}},
author = {Bshouty, Nader H. and Haddad-Zaknoon, Catherine A.},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {2016},
pages = {207-222},
doi = {10.1007/978-3-319-46379-7_14},
url = {https://mlanthology.org/alt/2016/bshouty2016alt-maximum/}
}