TY - GEN
T1 - Towards robust model selection using estimation and approximation error bounds
AU - Ratsaby, Joel
AU - Meir, Ronny
AU - Maiorov, Vitaly
PY - 1996
Y1 - 1996
N2 - One of the main problems in machine learning and statistical inference is selecting an appropriate model by which a set of data can be explained. A novel model selection criterion based on the uniform convergence of empirical processes combined with the results concerning the approximation ability of non-linear manifolds of functions is introduced. A coherent and robust framework for model selection was elucidated and a lower bound on the approximation error was established, giving a well specified sense for most functions of interest.
AB - One of the main problems in machine learning and statistical inference is selecting an appropriate model by which a set of data can be explained. A novel model selection criterion based on the uniform convergence of empirical processes combined with the results concerning the approximation ability of non-linear manifolds of functions is introduced. A coherent and robust framework for model selection was elucidated and a lower bound on the approximation error was established, giving a well specified sense for most functions of interest.
UR - https://www.scopus.com/pages/publications/0030383583
U2 - 10.1145/238061.238069
DO - 10.1145/238061.238069
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:0030383583
T3 - Proceedings of the Annual ACM Conference on Computational Learning Theory
SP - 57
EP - 67
BT - Proceedings of the Annual ACM Conference on Computational Learning TheoryPages 57 - 671996 Proceedings of the 1996 9th Annual Conference on Computational Learning Theory28 June 1996through 1 July 1996
T2 - Proceedings of the 1996 9th Annual Conference on Computational Learning Theory
Y2 - 28 June 1996 through 1 July 1996
ER -