CarpeDiem: an Algorithm for the Fast Evaluation of SSL Classifiers

CarpeDiem: an Algorithm for the Fast Evaluation of SSL Classifiers
Roberto Esposito - Università di Torino, Italy Daniele P. Radicioni - Università di Torino, Italy
In this paper we present a novel algorithm, CarpeDiem. It significantly improves on the time complexity of Viterbi algorithm, preserving the optimality of the result. This fact has consequences on Machine Learning systems that use Viterbi algorithm during learning or classification. We show how the algorithm applies to the Supervised Sequential Learning task and, in particular, to the HMPerceptron algorithm. We illustrate CarpeDiem in full details, and provide experimental results that support the proposed approach.

Roberto Esposito - Università di Torino, Italy
Daniele P. Radicioni - Università di Torino, Italy

In this paper we present a novel algorithm, CarpeDiem. It significantly improves on the time complexity of Viterbi algorithm, preserving the optimality of the result. This fact has consequences on Machine Learning systems that use Viterbi algorithm during learning or classification. We show how the algorithm applies to the Supervised Sequential Learning task and, in particular, to the HMPerceptron algorithm. We illustrate CarpeDiem in full details, and provide experimental results that support the proposed approach.