The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
This work provides a mathematical framework for improving linear classifiers, but it is incremental as it builds on existing perceptron-based methods.
The authors introduced the Maximum Cosine Framework (MCF) to derive new linear classifiers by bounding the cosine angle between target and algorithm functions, and developed the Maximum Cosine Perceptron (MCP) algorithm, which matches the Perceptron's mistake bound and outperforms PA and Aggressive ROMMA in single-pass learning on a real dataset.
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.