Trip Around the HMPerceptron Algorithm: Empirical Findings and Theoretical Tenets

Abstract. In a recent work we have carried out CarpeDiem, a novel algorithm for the fast evaluation of Supervised Sequential Learning (SSL) classifiers. In this paper we point out some interesting unexpected aspects of the learning behavior of the HMPerceptron algorithm that affect CarpeDiem performances. This observation is the starting point of an investigation about the internal working of the HMPerceptron, which unveils crucial details of the internal working of the HMPerceptron learning strategy. The understanding of these details, augment the comprehension of the algorithm meanwhile suggesting further enhancements.

• Type: Conference
• Year: 2007
• Where: AIIA
• Authors: Roberto Esposito, Daniele P. Radicioni
• Keywords: AIIA 2007, Artificial Intelligence, Interesting Unexpected Aspects, Internal Working, Supervised Sequential Learning