Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kernel-based L_2-Boosting with Structure Constraints   Developing efficient kernel methods for regression is very popular in the
past decade. In this paper, utilizing boosting on kernel-based weaker learners,
we propose a novel kernel-based learning algorithm called kernel-based
re-scaled boosting with truncation, dubbed as KReBooT. The proposed KReBooT
benefits in controlling the structure of estimators and producing sparse
estimate, and is near overfitting resistant. We conduct both theoretical
analysis and numerical simulations to illustrate the power of KReBooT.
Theoretically, we prove that KReBooT can achieve the almost optimal numerical
convergence rate for nonlinear approximation. Furthermore, using the recently
developed integral operator approach and a variant of Talagrand's concentration
inequality, we provide fast learning rates for KReBooT, which is a new record
of boosting-type algorithms. Numerically, we carry out a series of simulations
to show the promising performance of KReBooT in terms of its good
generalization, near over-fitting resistance and structure constraints.

31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
9
The categories of this paper are: nlin