Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accelerated learning algorithms of general fuzzy min-max neural network
  using a novel hyperbox selection rule   This paper proposes a method to accelerate the training process of a general
fuzzy min-max neural network. The purpose is to reduce the unsuitable
hyperboxes selected as the potential candidates of the expansion step of
existing hyperboxes to cover a new input pattern in the online learning
algorithms or candidates of the hyperbox aggregation process in the
agglomerative learning algorithms. Our proposed approach is based on the
mathematical formulas to form a branch-and-bound solution aiming to remove the
hyperboxes which are certain not to satisfy expansion or aggregation
conditions, and in turn, decreasing the training time of learning algorithms.
The efficiency of the proposed method is assessed over a number of widely used
data sets. The experimental results indicated the significant decrease in
training time of the proposed approach for both online and agglomerative
learning algorithms. Notably, the training time of the online learning
algorithms is reduced from 1.2 to 12 times when using the proposed method,
while the agglomerative learning algorithms are accelerated from 7 to 37 times
on average.

31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
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
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math