Title :
Comparison of machine learning methods for the sequence labelling applications
Author :
Amasyali, Mehmet Fatih ; Bilgin, Metin
Author_Institution :
Bilgisayar Muhendisligi, Yildiz Teknik Univ., İstanbul, Turkey
Abstract :
In this study, on artificial data sets, it was compared condition random fields(CRF) and classical machine learning(CML) types. First part of this study, the performances of CRF and CML types were measured on artificial data sets. As the result of studies, CML types, except Naive Bayes, performanced higher than CRF. The success of NR and CRF is high when the outputs consist of one distribution, in other case it stays low. Besides in this study, it was evaluated the effect of education set size on success. The second study was made to test this situation.
Keywords :
learning (artificial intelligence); pattern recognition; random processes; CML type; CRF; classical machine learning; condition random field; sequence labelling application; Bagging; Data mining; Data models; Hidden Markov models; Labeling; Niobium; Probabilistic logic; Conditional Random Fields; Sequence Labeling;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7129870
