Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Neural Network Classifier using Gradient-based Floating
  Centroid Method   Floating centroid method (FCM) offers an efficient way to solve a
fixed-centroid problem for the neural network classifiers. However,
evolutionary computation as its optimization method restrains the FCM to
achieve satisfactory performance for different neural network structures,
because of the high computational complexity and inefficiency. Traditional
gradient-based methods have been extensively adopted to optimize the neural
network classifiers. In this study, a gradient-based floating centroid (GDFC)
method is introduced to address the fixed centroid problem for the neural
network classifiers optimized by gradient-based methods. Furthermore, a new
loss function for optimizing GDFC is introduced. The experimental results
display that GDFC obtains promising classification performance than the
comparison methods on the benchmark datasets.

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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
18
The categories of this paper are: econ
3
The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci