Constant Rate Approximate Maximum Margin Algorithms

We present a new class of perceptron-like algorithms with margin in which the "effective" learning rate, defined as the ratio of the learning rate to the length of the weight vector, remains constant. We prove that the new algorithms converge in a finite number of steps and show that there exists a limit of the parameters involved in which convergence leads to classification with maximum margin.

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: Petroula Tsampouka, John Shawe-Taylor
• Keywords: ECML 2006, Learning Rate, Machine Learning, Perceptron-like Algorithms, Weight Vector