Support Vector Machines in Smile detection: A comparison of auto-tuning standard processes in Gaussian kernel
Resumo
Support Vector Machines are a set of machine learning models that have great performance in several tasks as well as on image classification and object recognition. However, the proper choice of model's hyperparameters has a great influence on the outcomes and the general capacity performance. In this paper, we explore some different traditional auto-tuning processes to estimate σ hyper-parameter for SVMs Gaussian kernel. These processes are common and also implemented on standard software of data science languages. The paper considers some different situations on smile detection. The results are composed by simulation study, two benchmark image applications and a real video data application.
Palavras-chave:
SVM, Gaussian Kernel, tuning, image detection, smile
Referências
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B. Caputo, K. Sim, F. Furesjo, and A. Smola. Appearance-based object recognition using svms: which kernel should i use? In NIPS Proceedings, Whistler, volume 2002, 2002.
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L. H. Hamel. Knowledge discovery with support vector machines, volume 3. John Wiley & Sons, 2011.
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in'Real-Life'Images: detection, alignment, and recognition, 2008.
T. Jebara. Multi-task feature and kernel selection for svms. In Proceedings of the twenty-first international conference on Machine learning, page 55, 2004.
D. E. King. Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research, 10:1755-1758, 2009.
P. Li, L. Dong, H. Xiao, and M. Xu. A cloud image detection method based on svm vector machine. Neurocomputing, 169:34-42, 2015.
M. Maia, J. S. Pimentel, I. S. Pereira, J. Gondim, M. E. Barreto, and A. Ara. Convolutional support vector models: Prediction of coronavirus disease using chest xrays. Information, 11(12):548, 2020.
R. G. Mantovani, A. L. Rossi, J. Vanschoren, B. Bischl, and A. C. De Carvalho. Effectiveness of random search in svm hyper-parameter tuning. In 2015 International Joint Conference on Neural Networks (IJCNN), pages 1-8. Ieee, 2015.
U. Maulik and D. Chakraborty. Remote sensing image classification: A survey of support-vector-machine-based advanced techniques. IEEE Geoscience and Remote Sensing Magazine, 5(1):33-52, 2017.
R. Mishra, S. Meher, N. Kustha, and T. Pradhan. A skin cancer image detection interface tool using vlf support vector machine classification. In Computational Intelligence in Pattern Recognition, pages 49-63. Springer, 2022.
J. Platt et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61- 74, 1999.
C. Savas and F. Dovis. The impact of different kernel functions on the performance of scintillation detection based on support vector machines. Sensors, 19(23):5219, 2019.
Scikit-learn. RBF SVM parameters. [link], 2008. [Online; accessed 10-October-2021].
P. K. Shivaswamy, W. Chu, and M. Jansche. A support vector approach to censored targets. In Seventh IEEE International Conference on Data Mining (ICDM 2007), pages 655-660. IEEE, 2007.
W. Wang, Z. Xu, W. Lu, and X. Zhang. Determination of the spread parameter in the gaussian kernel for classification and regression. Neurocomputing, 55(3-4):643-663, 2003.
G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
G. W. Brier. Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78:1-3, 1950.
B. Caputo, K. Sim, F. Furesjo, and A. Smola. Appearance-based object recognition using svms: which kernel should i use? In NIPS Proceedings, Whistler, volume 2002, 2002.
C.-C. Chang and C.-J. Lin. Libsvm: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3):1-27, 2011.
C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273-297, 1995.
L. H. Hamel. Knowledge discovery with support vector machines, volume 3. John Wiley & Sons, 2011.
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in'Real-Life'Images: detection, alignment, and recognition, 2008.
T. Jebara. Multi-task feature and kernel selection for svms. In Proceedings of the twenty-first international conference on Machine learning, page 55, 2004.
D. E. King. Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research, 10:1755-1758, 2009.
P. Li, L. Dong, H. Xiao, and M. Xu. A cloud image detection method based on svm vector machine. Neurocomputing, 169:34-42, 2015.
M. Maia, J. S. Pimentel, I. S. Pereira, J. Gondim, M. E. Barreto, and A. Ara. Convolutional support vector models: Prediction of coronavirus disease using chest xrays. Information, 11(12):548, 2020.
R. G. Mantovani, A. L. Rossi, J. Vanschoren, B. Bischl, and A. C. De Carvalho. Effectiveness of random search in svm hyper-parameter tuning. In 2015 International Joint Conference on Neural Networks (IJCNN), pages 1-8. Ieee, 2015.
U. Maulik and D. Chakraborty. Remote sensing image classification: A survey of support-vector-machine-based advanced techniques. IEEE Geoscience and Remote Sensing Magazine, 5(1):33-52, 2017.
R. Mishra, S. Meher, N. Kustha, and T. Pradhan. A skin cancer image detection interface tool using vlf support vector machine classification. In Computational Intelligence in Pattern Recognition, pages 49-63. Springer, 2022.
J. Platt et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61- 74, 1999.
C. Savas and F. Dovis. The impact of different kernel functions on the performance of scintillation detection based on support vector machines. Sensors, 19(23):5219, 2019.
Scikit-learn. RBF SVM parameters. [link], 2008. [Online; accessed 10-October-2021].
P. K. Shivaswamy, W. Chu, and M. Jansche. A support vector approach to censored targets. In Seventh IEEE International Conference on Data Mining (ICDM 2007), pages 655-660. IEEE, 2007.
W. Wang, Z. Xu, W. Lu, and X. Zhang. Determination of the spread parameter in the gaussian kernel for classification and regression. Neurocomputing, 55(3-4):643-663, 2003.
Publicado
22/11/2021
Como Citar
GONDIM, João; MAIA, Mateus; ROCHA, Ana Caroline Lopes; ARGOLO, Felipe; ARA, Anderson; LOCH, Alexandre Andrade.
Support Vector Machines in Smile detection: A comparison of auto-tuning standard processes in Gaussian kernel. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 118-123.
DOI: https://doi.org/10.5753/wvc.2021.18900.
