1 research outputs found
The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
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