Paper: 347582
Title: Bayesian regularization of function approximation using orthogonolized basis

-------------------- review 1 --------------------
---------------------------- REVIEW 1 --------------------------
PAPER: 8
TITLE: Bayesian regularization of function approximation using orthogonolized basis
 
OVERALL RATING: 2 (accept) 
REVIEWER'S CONFIDENCE: 4 (expert) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 4 (good)
English readability: 4 (good)
Paper organization/presentation: 4 (good)
Has good survey been done?: 4 (good)


Comparisons with the literature should be presented more in detail.
Results should be discussed more. 



-------------------- review 2 --------------------
---------------------------- REVIEW 2 --------------------------
PAPER: 8
TITLE: Bayesian regularization of function approximation using orthogonolized basis
 
OVERALL RATING: 2 (accept) 
REVIEWER'S CONFIDENCE: 3 (high) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 3 (fair)
English readability: 3 (fair)
Paper organization/presentation: 3 (fair)
Has good survey been done?: 4 (good)


The heart of the problem of scalar function approximation is to build a functional dependence which is able to produce the given dataset. Functional approximation, as most of the inverse problems, is ill-posed. In this paper, a fair survey of the problem is given, first. A point-by-point approximation method for multidimensional scalar function is discussed. And the orthogonalized basis is used for Bayesian regularization of function approximation. The proposed approximation algorithm provides unique analytical solution for the regularization parameters. This study is relevant to this conference. However, the English writing in the paper needs to be further improved or correct.