Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Longitudinal Support Vector Machines for High Dimensional Time Series   We consider the problem of learning a classifier from observed functional
data. Here, each data-point takes the form of a single time-series and contains
numerous features. Assuming that each such series comes with a binary label,
the problem of learning to predict the label of a new coming time-series is
considered. Hereto, the notion of {\em margin} underlying the classical support
vector machine is extended to the continuous version for such data. The
longitudinal support vector machine is also a convex optimization problem and
its dual form is derived as well. Empirical results for specified cases with
significance tests indicate the efficacy of this innovative algorithm for
analyzing such long-term multivariate data.

25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org