Cite
Leave-one-out-training and leave-one-out-testing hidden markov models for a handwritten numeral recognizer: the implications of a single classifier and multiple classifications.
MLA
Ko, Albert Hung-Ren, et al. “Leave-One-out-Training and Leave-One-out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, Dec. 2009, pp. 2168–78. EBSCOhost, https://doi.org/10.1109/TPAMI.2008.254.
APA
Ko, A. H.-R., Cavalin, P. R., Sabourin, R., & de Souza Britto, A. (2009). Leave-one-out-training and leave-one-out-testing hidden markov models for a handwritten numeral recognizer: the implications of a single classifier and multiple classifications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2168–2178. https://doi.org/10.1109/TPAMI.2008.254
Chicago
Ko, Albert Hung-Ren, Paulo Rodrigo Cavalin, Robert Sabourin, and Alceu de Souza Britto. 2009. “Leave-One-out-Training and Leave-One-out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications.” IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12): 2168–78. doi:10.1109/TPAMI.2008.254.