Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A new bandwidth selection criterion for using SVDD to analyze
  hyperspectral data   This paper presents a method for hyperspectral image classification that uses
support vector data description (SVDD) with the Gaussian kernel function. SVDD
has been a popular machine learning technique for single-class classification,
but selecting the proper Gaussian kernel bandwidth to achieve the best
classification performance is always a challenging problem. This paper proposes
a new automatic, unsupervised Gaussian kernel bandwidth selection approach
which is used with a multiclass SVDD classification scheme. The performance of
the multiclass SVDD classification scheme is evaluated on three frequently used
hyperspectral data sets, and preliminary results show that the proposed method
can achieve better performance than published results on these data sets.

22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th