Abstract
The propose to improve a Support Vector Machines (SVM) learning accuracy by using a Real Adaboost algorithm for selecting features is presented in the chapter. This technique aims to minimize the recognition error rates and the computational effort. Hence, the Real Adaboost will be used not as classifier but as a technique for selecting features in order to keep only the relevant features that will be used to improve our systems accuracy. Since the Real Adaboost is only used for binary classifications problems, we investigate different ways of combining selected features applied to a multi-class classification task. To experiment this selection, we use the phoneme datasets from TIMIT corpus [Massachusetts Institute of Technology (MIT), SRI International and Texas Instruments, Inc. (TI)] and Mel-Frequency Cepstral Coefficients (MFCC) feature representations. It must be pointed out that before using the Real Adaboost the multi-class phoneme recognition problem should be converted into a binary one.
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References
Amami R, Ben Ayed D, Ellouze N (2012) Phoneme recognition using support vector machine and different features representations. Adv Intell Soft Comput 151:587–595
Amami R, Ben Ayed D, Ellouze N (2013a) Practical selection of SVM supervised parameters with different feature representations for vowel recognition. Int J Digit Content Technol Appl 7(9):418–424
Amami R, Ben Ayed D, Ellouze N (2013b) Adaboost with SVM using GMM supervector for imbalanced phoneme data. In: Proceedings of 6th International IEEE conference on human system interaction, pp 328–333
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Cover T (1974) The best two independent measurements are not the two best. IEEE Trans Syst Man Cybern 4:116–117
Davis SB, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust, Speech, Signal Proc 28:357–366
Freund Y, Schapire RE (1997) A short introduction to boosting. J Japan Soc Artif Intell 14(5):771–780
Freund Y, Schapire RE (1995) A decision-theoretic generalization of online learning and an application to boosting. In: Proceedings of 2nd European conference on computational learning theory, pp 23–37
Garofolo JS, Lamel LF, Fisher WM, Fiscus JG, Pallett DS, Dahlgren NL, Zue V (1993) Timit acoustic-phonetic continuous speech corpus. Texas instruments and massachusetts institute of technology
Li F, Košecká J, Wechsler H (2006) Strangeness based feature selection for part based recognition. In: IEEE conference on computer vision and pattern recognition
Li X, Wang L, Sung E (2008) Adaboost with SVM-based compnent classifers. Eng Appl Artif Intell 21:785–795
Lienhart R, Maydt J (2002) An extended set of Haar-like features for rapid object detection. In: Proceedings of IEEE conference on image processing, pp 900–903
Michael J, Viola P (2003) Face recognition using boosted local features. In: Proceedings of international conference on computer vision, pp 1–8
Meir R, Ratsch G (2003) An introduction to boosting and leveraging. Advanced lectures on machine learning, Springer, Berlin, pp 119–184
Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336
Quinlan JR (1996) Bagging, boosting, and c4.5. In: Proceedings of 13th national conference on artificial intelligence, pp 725–730
Torralba A, Murphy K, Freeman W (2004) Sharing features: efficient boosting procedures for multiclass object detection. In: IEEE conference on computer vision and pattern recognition, pp 762–769
Vezhnevets A, Vezhnevets V (2005) Modest adaboost—teaching adaboost to generalize better. Graphicon, pp 322–325
Viola P, Jones M (2001) Robust real-time face detection. J Comput Vision 2:137–154
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Amami, R., Ayed, D.B., Ellouze, N. (2014). Feature Selection Using Adaboost for Phoneme Recognition. In: S. Hippe, Z., L. Kulikowski, J., Mroczek, T., Wtorek, J. (eds) Issues and Challenges in Artificial Intelligence. Studies in Computational Intelligence, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-319-06883-1_4
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