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The representer theorem for Hilbert spaces: a necessary and sufficient
  condition
v1v2v3 (latest)

The representer theorem for Hilbert spaces: a necessary and sufficient condition

Neural Information Processing Systems (NeurIPS), 2012
9 May 2012
Francesco Dinuzzo
Bernhard Schölkopf
ArXiv (abs)PDFHTML

Papers citing "The representer theorem for Hilbert spaces: a necessary and sufficient condition"

15 / 15 papers shown
Title
Time-domain sound field estimation using kernel ridge regression
Time-domain sound field estimation using kernel ridge regression
Jesper Brunnström
M. B. Møller
Jan Østergaard
Shoichi Koyama
Toon van Waterschoot
M. Moonen
78
2
0
06 Sep 2025
Advancing Supervised Learning with the Wave Loss Function: A Robust and
  Smooth Approach
Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach
M. Akhtar
Muhammad Tanveer
Mohd. Arshad
211
26
0
28 Apr 2024
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for
  Supervised Learning
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised LearningIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
M. Akhtar
Muhammad Tanveer
Mohd. Arshad
204
24
0
05 Sep 2023
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
Firas Laakom
Jenni Raitoharju
Nikolaos Passalis
Alexandros Iosifidis
Moncef Gabbouj
UD
125
0
0
09 Feb 2022
Kernel Learning For Sound Field Estimation With L1 and L2
  Regularizations
Kernel Learning For Sound Field Estimation With L1 and L2 RegularizationsIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021
Ryosuke Horiuchi
Shoichi Koyama
Juliano G. C. Ribeiro
Natsuki Ueno
Hiroshi Saruwatari
71
15
0
11 Oct 2021
The Mathematical Foundations of Manifold Learning
The Mathematical Foundations of Manifold Learning
Luke Melas-Kyriazi
AI4CE
159
20
0
30 Oct 2020
Geometric Interpretation of Running Nyström-Based Kernel Machines
  and Error Analysis
Geometric Interpretation of Running Nyström-Based Kernel Machines and Error Analysis
Weida Li
Mingxia Liu
Daoqiang Zhang
199
0
0
20 Feb 2020
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for
  Multi-Label Classification
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label ClassificationNeural Networks (NN), 2019
Guoqiang Wu
Ruobing Zheng
Ying-jie Tian
Dalian Liu
97
103
0
05 Nov 2019
Approximate Representer Theorems in Non-reflexive Banach Spaces
Approximate Representer Theorems in Non-reflexive Banach SpacesInternational Conference on Algorithmic Learning Theory (ALT), 2019
Kevin Schlegel
45
2
0
01 Nov 2019
A Generalized Representer Theorem for Hilbert Space - Valued Functions
A Generalized Representer Theorem for Hilbert Space - Valued Functions
S. Diwale
Colin N. Jones
89
6
0
19 Sep 2018
Robust Matrix Elastic Net based Canonical Correlation Analysis: An
  Effective Algorithm for Multi-View Unsupervised Learning
Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning
Pengbo Zhang
Zhixin Yang
CML
149
1
0
14 Nov 2017
Equivalence of Learning Algorithms
Equivalence of Learning Algorithms
Julien Audiffren
Hachem Kadri
110
1
0
10 Jun 2014
M-Power Regularized Least Squares Regression
M-Power Regularized Least Squares RegressionIEEE International Joint Conference on Neural Network (IJCNN), 2013
Julien Audiffren
Hachem Kadri
144
1
0
09 Oct 2013
Learning Output Kernels for Multi-Task Problems
Learning Output Kernels for Multi-Task Problems
Francesco Dinuzzo
160
26
0
16 Jan 2013
Learning from Distributions via Support Measure Machines
Learning from Distributions via Support Measure MachinesNeural Information Processing Systems (NeurIPS), 2012
Krikamol Muandet
Kenji Fukumizu
Francesco Dinuzzo
Bernhard Schölkopf
316
199
0
29 Feb 2012
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