References
- K. Grauman and T. Darrell, "The pyramid match kernel Discriminative classification with sets of image features", Proceedings of IEEE International Conference on Computer Vision, 2005
- Y. H. Kang, Y. B. Park, "Design of Automatic Document Classifier for IT documents based on SVM, " j.inst.Korean.electr.electron.eng, Vol. 8, No. 2 186-194, 2004
- J. Platt, "Fast Training of Support Vector Machines Using Sequential Minimal Optimization," in Advances in kernel methods - Support Vector Learning, MIT Press, 185-208, 1999
- C. Chang and C. Lin, "LIBSVM : A Library for Support Vector Machine," ACM Transactions on Intelligent Systems and Technology, Vol 2, No.3, 1-27, 2011
- S. Cadambi, I. Srihari, et al. "A massively parallel FPGA-based coprocessor for support vector machines," Field Programmable Custom Computing Machines '09. 17th IEEE Symposium on, Napa, USA, 2009
- T. Kuan, J. Wang, J. Wang, P. Lin, G. Gu, "VLSI Design of an SVM Learning Core on Sequential Minimal Optimization Algorithm," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 20, No. 4, 673-683, 2012 https://doi.org/10.1109/TVLSI.2011.2107533
- M. Qasaimeh, A. Sagahyroon, and T. Shanableh, "FPGA-Based Parallel Hardware Architecture for Real-Time Image Classification," Computational Imaging, IEEE Transactions, Vol. 1, No. 1 56-70, 2015 https://doi.org/10.1109/TCI.2015.2424077
- Marta Ruiz-Llata, Guillermo Guarnizo and Mar Yebenes-Calvino, "FPGA Implementation of a Support Vector Machine for Classification and Regression," IJCNN, 2010
- Xipeng Pan, Huihua Yang, Lingqiao Li, Zhenbing Liu, Le Hou , "FPGA Implementation of SVM Decision Function Based on Hardware-friendly Kernel," ICCIS, 2013
- Bryan Catanzaro, "Fast Support Vector Machine Training and Classification on Graphics Processors," IMAGING, Vol. 1, No. 1, March 2015
- Christos Kyrkou, Theocharis Theocharides, "A Parallel Hardware Architecture for Real-Time Object Detection with Support Vector Machines," IEEE TRANSACTIONS ON COMPUTERS, Vol. 61, No. 6, 2012
- Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, "A Practical Guide to Support Vector Classification," Technical Report, Department of Computer Science National Taiwan University, 2003
- W. S. Na, S. W. Han, Y. J. Jeong, "FPGA Design of SVM Classifier for Real Time Image Processing," j.inst.Korean.electr.electron.eng, Vol. 20, No. 3 209-219, 2016
- C. Cortes and V. Vapnik, "Support-Vector Network," Machine Learning, Vol. 20, No. 3 273-297, 1995 https://doi.org/10.1007/BF00994018
- Il-Seok Oh, Pattern Recognition, Kyobomoongo, 137-173, 2008
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2008
- V. Vapnik, Estimation of Dependances Based on Empirical Data, Springer-Verlag, 1982
- E. Osuna, R. Freund, F. Girosi, "An improved training algorithm for support vector machines," Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Workshop, 276-285, 1997
- J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, "Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition," Neural Networks, vol. 32, pp. 323-332, 2012. https://doi.org/10.1016/j.neunet.2012.02.016
- Tomasz Kryjak, Mateusz Komorkiewicz, Marek Gorgon, "FPGA IMPLEMENTATION OF REAL-TIME HEAD-SHOULDER DETECTION USING LOCAL BINARY PATTERNS, SVM AND FOREGROUND OBJECT DETECTION," Design and Architectures for Signal and Image Processing (DASIP) Conference, Karlsruhe, Germany, 2012