Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 206,145,711 papers from all fields of science
Search
Sign In
Create Free Account
Bayesian interpretation of kernel regularization
Known as:
Bayesian interpretation of regularization
In machine learning, kernel methods arise from the assumption of an inner product space or similarity structure on inputs. For some such methods…
Expand
Wikipedia
Create Alert
Alert
Related topics
Related topics
8 relations
Gaussian process
Gramian matrix
Hilbert space
Kernel methods for vector output
Expand
Broader (1)
Machine learning
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint
G. Pillonetto
,
Tianshi Chen
,
A. Chiuso
,
G. Nicolao
,
L. Ljung
Autom.
2016
Corpus ID: 12457124
2016
2016
A new kernel-based approach to hybrid system identification
G. Pillonetto
Autom.
2016
Corpus ID: 35486529
2015
2015
Identification of hybrid systems using stable spline kernels
G. Pillonetto
IEEE 25th International Workshop on Machine…
2015
Corpus ID: 11342271
All the approaches for identification of hybrid systems appeared in the literature assume known the model complexity. Widely used…
Expand
Highly Cited
2004
Highly Cited
2004
Bayesian modeling of uncertainty in low-level vision
R. Szeliski
International Journal of Computer Vision
2004
Corpus ID: 8433503
The need for error modeling, multisensor fusion, and robust algorithms is becoming increasingly recognized in computer vision…
Expand
2003
2003
Regularization networks for inverse problems: A state-space approach
G. Nicolao
,
G. Ferrari-Trecate
Autom.
2003
Corpus ID: 15105074
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
,
Terms of Service
, and
Dataset License
ACCEPT & CONTINUE