Sequential Error Rate evaluation of SSVEP classification problem with Bayesian sequential learning

Autor:	Katsuki Nakamura, Hideyuki Hara, Atsushi Takemoto, Takashi Matsumoto, Yumi Dobashi
Rok vydání:	2010
Předmět:	Computer Science::Machine Learning Wake-sleep algorithm Computer science business.industry Bayesian probability Monte Carlo method Word error rate Pattern recognition Bayesian inference Time–frequency analysis Sequence learning Artificial intelligence Particle filter business
Zdroj:	Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.
Popis:	An attempt was made to evaluate the Sequential Error Rate (SER) of an SSVEP classification problem with a Bayesian sequential learning algorithm. Sequential Error Rate refers to the average classification error rate windowed over a short trial period. The algorithm was implemented by the Sequential Monte Carlo method. As opposed to the batch learning algorithm, the sequential learning algorithm does not divide the data into training and test datasets; rather, it starts learning with the first single trial data and proceeds with the learning sequentially using the rest of the data. The algorithm was tested against an SSVEP classification problem. The algorithm appeared functional.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::00fa111682a6a54349a5b4f097a5b73e https://doi.org/10.1109/itab.2010.5687773 Zobrazit plný text záznamu