Convolutional kernel networks based on a convex combination of cosine kernels

Băzăvan, 2012, Fourier kernel learning, 459 Bengio, 2007, Scaling learning algorithms towards ai, Large-scale Kernel Mach., 34, 1 Bo, 2011, Object recognition with hierarchical kernel descriptors, 1729 Bo, 2010, Kernel descriptors for visual recognition, 244 Bo, 2009, Efficient match kernel between sets of features for visual recognition, 135 Bochner, 1933, Monotone funktionen, stieltjessche integrale und harmonische analyse, Mathematische Annalen, 108, 378, 10.1007/BF01452844 Byrd, 1995, A limited memory algorithm for bound constrained optimization, SIAM J. Sci. Comput., 16, 1190, 10.1137/0916069 Camastra, 2006, Offline cursive character challenge: a new benchmark for machine learning and pattern recognition algorithms, 2, 913 Chan, 2015, Pcanet: a simple deep learning baseline for image classification?, IEEE Trans. Image Process., 24, 5017, 10.1109/TIP.2015.2475625 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Dasgupta, 2016, A holistic approach for off-line handwritten cursive word recognition using directional feature based on arnold transform, Pattern Recognit. Lett., 79, 73, 10.1016/j.patrec.2016.05.017 L. Dong, L. He, G. Kong, Q. Zhang, X. Cao, E. Izquierdo, Cunet: a compact unsupervised network for image classification, arXiv:1607.01577 (2016). Fan, 2008, Liblinear: a library for large linear classification, J. Mach. Learn. Res., 9, 1871 Gatto, 2017, Discriminative canonical correlation analysis network for image classification, 4487 Ghiasi-Shirazi, 2010, Learning translation invariant kernels for classification, J. Mach. Learn. Res., 11, 1353 Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527 Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: convolutional architecture for fast feature embedding, arXiv:1408.5093 (2014). A. Krizhevsky, G. Hinton, Learning Multiple Layers of Features from Tiny Images (2009). Lazebnik, 2006, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, 2, 2169 LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 Lyu, 2005, Mercer kernels for object recognition with local features, 2, 223 Mairal, 2016, End-to-end kernel learning with supervised convolutional kernel networks, 1399 Mairal, 2014, Convolutional kernel networks, 2627 Montavon, 2011, Kernel analysis of deep networks, J. Mach. Learn. Res., 12, 2563 A. Morrow, V. Shankar, D. Petersohn, A. Joseph, B. Recht, N. Yosef, Convolutional kitchen sinks for transcription factor binding site prediction, arXiv:1706.00125 (2017). Mukuta, 2016, Kernel approximation via empirical orthogonal decomposition for unsupervised feature learning, 5222 Ng, 2015, Dctnet: a simple learning-free approach for face recognition, 761 Paulin, 2017, Convolutional patch representations for image retrieval: an unsupervised approach, Int. J. Comput. Vis., 121, 149, 10.1007/s11263-016-0924-3 Phillips, 2000, The feret evaluation methodology for face-recognition algorithms, IEEE Trans. Pattern Anal. Mach. Intell., 22, 1090, 10.1109/34.879790 Rahimi, 2008, Random features for large-scale Kernel machines, 1177 Scholkopf, 2001 K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556 (2014). F.X. Yu, S. Kumar, H. Rowley, S.-F. Chang, Compact nonlinear maps and circulant extensions, arXiv:1503.03893 (2015). Zeiler, 2014, Visualizing and understanding convolutional networks, 818