Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Additive Models   We present a new class of methods for high-dimensional nonparametric
regression and classification called sparse additive models (SpAM). Our methods
combine ideas from sparse linear modeling and additive nonparametric
regression. We derive an algorithm for fitting the models that is practical and
effective even when the number of covariates is larger than the sample size.
SpAM is closely related to the COSSO model of Lin and Zhang (2006), but
decouples smoothing and sparsity, enabling the use of arbitrary nonparametric
smoothers. An analysis of the theoretical properties of SpAM is given. We also
study a greedy estimator that is a nonparametric version of forward stepwise
regression. Empirical results on synthetic and real data are presented, showing
that SpAM can be effective in fitting sparse nonparametric models in high
dimensional data.

36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph