# The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers

arXiv: https://arxiv.org/abs/1707.00821 · HTML: https://mlsanity.com/paper/1707.00821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Efficient Online Large-Margin Classification via Dual Certificates](https://mlsanity.com/paper/2509.19670/) (2025)

## Abstract (arXiv)

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. 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. 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 and Aggressive ROMMA.