Yazar "Polat G." seçeneğine göre listele
Listeleniyor 1 - 3 / 3
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Determining efficiency of speech feature groups in emotion detection [Ses özni·teli·k gruplarinin duygu tespi·ti·nde etki·nli·kleri·ni·n beli·rlenmesi·](2007) Polat G.; Altun H.Features, extract from speech parameter are frequently used in emotion detection problem. Prosodic, MFCC, LPC and band energy feature groups are commonly used in literature to detect emotion in speech. The aim of the study is to examine the efficiency of these features groups in emotion detection problem using a SVM classifier.Öğe Evalutation of performance of KNN, MLP and RBF classifiers in emotion detection problem [Duygu tespi·t problemi·nde KNN, MLP ve RBF siniflandiricilarin başarimlarinin degerlendi·ri·lmesi·](2007) Polat G.; Altun H.Emotion Detection has gained increasing attention and become an active research area. The problem is solved with improved feature set with different number of feature groups, by employing different classifiers in order to achieve satisfactory recognition rate. In this study, speech related features are employed to evaluate the performance of different classifiers in emotion detection problem.Öğe On the comparison of classifiers' performance in emotion classification: Critiques and suggestions [Duygu siniflandirma problemlerinde siniflandirici performanslarinin karşilaştirilmasi: Eleştiri ve öneriler](2008) Altun H.; Polat G.In literature there is a huge body of references available which compare various classifiers in a particular application. However, the reliability of such a comparison is only valid if the model parameters, performance criteria and training environment are chosen in a fair framework, as successful application of a classifier is dependent on the those parameters. In this study we attempt to answer the questions below in a emotion detection framework, using classifiers such as KNN, SVM, RBF and MLP: Is the success of a classifier enough to make the claim that a classifier is "the best one" in a particular classification task? How is it possible to carry out a fair comparison between classifiers? ©2008 IEEE.
