Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Pantelis Bouboulis"'
Autor:
Jian Yang, V. Sree Hari Rao, Björn Schuller, Carlotta Domeniconi, Yi Shen, Pantelis Bouboulis, Cong Wang, Shiro Ikeda, Huajin Tang, Patricia Melin, Derong Liu, Xuelong Li, Bart Baesens, Dongbin Zhao, Sergio Cruces
This issue marks the beginning of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS). By adding "Learning Systems" to the title, we now state explicitly the scope of the TRANSACTIONS to include neural networks as well as related le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ef4ea74dd09520f8bc979604c1df7c2
https://opus.bibliothek.uni-augsburg.de/opus4/files/73305/73305.pdf
https://opus.bibliothek.uni-augsburg.de/opus4/files/73305/73305.pdf
Publikováno v:
IEEE Transactions on Signal Processing. 66:1920-1932
We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parame
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9590c2c220c2f7af5511c82bb26d90c8
https://doi.org/10.1109/tsp.2017.2781640
https://doi.org/10.1109/tsp.2017.2781640
A Random Fourier Features Perspective of KAFs With Application to Distributed Learning Over Networks
A major problem in any typical online kernel-based scheme is that the model's solution is given as an expansion of kernel functions that grows linearly with time. Usually, some sort of pruning strategy is adopted to make the solution sparse for pract
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae22a13a2c4ae3a957d8ca1a2e1e85ca
https://doi.org/10.1016/b978-0-12-812976-0.00009-9
https://doi.org/10.1016/b978-0-12-812976-0.00009-9
Autor:
Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz, Theodore W. Berger, Pantelis Bouboulis, Renato Candido, Alberto Carini, Stefania Cecchi, Badong Chen, Jie Chen, Symeon Chouvardas, Danilo Comminiello, Konstantinos Diamantaras, Georgios B. Giannakis, Christian Hofmann, Vassilis N. Ioannidis, Walter Kellermann, Bahare Kiumarsi, Suleyman Serdar Kozat, S.Y. Kung, Frank L. Lewis, Zhe Li, Derong Liu, Biao Luo, Meng Ma, Danilo P. Mandic, Tomas McKelvey, Hamidreza Modares, Athanasios N. Nikolakopoulos, Simone Orcioni, Raffaele Parisi, José C. Príncipe, Cédric Richard, Brian S. Robinson, Daniel Romero, Simone Scardapane, Michele Scarpiniti, Magno T.M. Silva, Dong Song, Sergios Theodoridis, Aurelio Uncini, Kyriakos G. Vamvoudakis, Nuri Denizcan Vanli, Xin Wang, Huai-Ning Wu, Yili Xia, Min Xiang, Yinan Yu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c20daf2f46fdc1880e594dc62f5ace45
https://doi.org/10.1016/b978-0-12-812976-0.00024-5
https://doi.org/10.1016/b978-0-12-812976-0.00024-5
Publikováno v:
SSP
We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS (e.g., kernel trick), we map the data to a finite dimensional Euclidean space, using random f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd959c7b6abc4f5379428f70ddb134ca
http://arxiv.org/abs/1606.03685
http://arxiv.org/abs/1606.03685
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 23:260-276
This paper introduces a wide framework for online, i.e., time-adaptive, supervised multiregression tasks. The problem is formulated in a general infinite-dimensional reproducing kernel Hilbert space (RKHS). In this context, a fairly large number of n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f2e4d40279ea2d5e12a6e4d0f331570
https://doi.org/10.1109/tnnls.2011.2178321
https://doi.org/10.1109/tnnls.2011.2178321
Publikováno v:
Journal of Computational and Applied Mathematics. 235:3425-3434
Reproducing Kernel Hilbert Spaces (RKHSs) are a very useful and powerful tool of functional analysis with application in many diverse paradigms, such as multivariate statistics and machine learning. Fractal interpolation, on the other hand, is a rela
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::913b5af48993dd876cf9af28c210eb85
https://doi.org/10.1016/j.cam.2011.02.003
https://doi.org/10.1016/j.cam.2011.02.003
Publikováno v:
IEEE Transactions on Signal Processing. 59:964-978
Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. The primary mathematical tool employed in these methods is the notion of the Reproducing Kernel Hilbert Space. However, so fa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::373df9dfd806c458e0edbf6ecf96d753
https://doi.org/10.1109/tsp.2010.2096420
https://doi.org/10.1109/tsp.2010.2096420
Autor:
Pantelis Bouboulis, Leoni Dalla
Publikováno v:
Journal of Mathematical Analysis and Applications. 336:919-936
Based on the construction of Fractal Interpolation Functions, a new construction of Fractal Interpolation Surfaces on arbitrary data is presented and some interesting properties of them are proved. Finally, a lower bound of their box counting dimensi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b54f12319703784d4a3f7dc73211b081
https://doi.org/10.1016/j.jmaa.2007.01.112
https://doi.org/10.1016/j.jmaa.2007.01.112
Autor:
Leoni Dalla, Pantelis Bouboulis
Publikováno v:
European Journal of Applied Mathematics. 18:449-476
We generalise the notion of fractal interpolation functions (FIFs) to allow data sets of the form where I=[0,1]n. We introduce recurrent iterated function systems whose attractors G are graphs of continuous functions f:I→, which interpolate the dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c7c63f5896fa2f19a81d8d85b473285
https://doi.org/10.1017/s0956792507007024
https://doi.org/10.1017/s0956792507007024