Title :
Classifier combination for telegraphese restoration
Author :
Santoso, Leo Willyanto
Author_Institution :
Dept. of Comput. Sci., Petra Christian Univ., Surabaya, Indonesia
Abstract :
This paper presents a classifier combination to solve telegraphese restoration problem. By implementing more than one classifier, it can support other classifier, and finally it can improve the performance. Using supplied development data, training data and testing data, the best model had an accuracy F = 79 %.
Keywords :
learning (artificial intelligence); pattern classification; classifier combination; development data supply; telegraphese restoration; testing data; training data; Accuracy; Classification algorithms; Machine learning; Machine learning algorithms; Speech; Tagging; Training; Classifier combination; Penn Treebank tagset; chunk parsing; telegraphese restoration;
Conference_Titel :
Uncertainty Reasoning and Knowledge Engineering (URKE), 2011 International Conference on
Conference_Location :
Bali
Print_ISBN :
978-1-4244-9985-4
Electronic_ISBN :
978-1-4244-9984-7
DOI :
10.1109/URKE.2011.6007844
