Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wavelet Time Shift Properties Integration with Support Vector Machines   This paper presents a short evaluation about the integration of information
derived from wavelet non-linear-time-invariant (non-LTI) projection properties
using Support Vector Machines (SVM). These properties may give additional
information for a classifier trying to detect known patterns hidden by noise.
In the experiments we present a simple electromagnetic pulsed signal
recognition scheme, where some improvement is achieved with respect to previous
work. SVMs are used as a tool for information integration, exploiting some
unique properties not easily found in neural networks.

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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci