@inproceedings{01aad67165ca4430a0edf54547abe553,
title = "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{\textquoteright}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.",
keywords = "Linear classifiers, Online learning, Perceptron",
author = "Bshouty, {Nader H.} and Haddad-Zaknoon, {Catherine A.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 27th International Conference on Algorithmic Learning Theory, ALT 2016 ; Conference date: 19-10-2016 Through 21-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46379-7_14",
language = "الإنجليزيّة",
isbn = "9783319463780",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "207--222",
editor = "Simon, {Hans Ulrich} and Sandra Zilles and Ronald Ortner",
booktitle = "Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings",
}