Perceptron Learning with Random Coordinate Descent

A perceptron is a linear threshold classier that separates examples with a hyperplane. It is perhaps the simplest learning model that is used standalone. In this paper, we propose a family of random coordinate descent algorithms for perceptron learning on binary classication problems. Un- like most perceptron learning algorithms, which require smooth cost functions, our algorithms directly minimize the training error, and usually achieve the lowest training error compared with other algo- rithms. The algorithms are also computationally ecien t. Such advantages make them favorable for both standalone use and ensemble learning, on problems that are not linearly separable. Experiments show that our algorithms work very well with AdaBoost, and achieve the lowest test errors for half of the data sets.