Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Twin Neural Networks for Classification of Unbalanced Data   Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative
to Support Vector Machines (SVM) for learning from imbalanced datasets. The
TWSVM learns two non-parallel classifying hyperplanes by solving a couple of
smaller sized problems. However, it is unsuitable for large datasets, as it
involves matrix operations. In this paper, we discuss a Twin Neural Network
(Twin NN) architecture for learning from large unbalanced datasets. The Twin NN
also learns an optimal feature map, allowing for better discrimination between
classes. We also present an extension of this network architecture for
multiclass datasets. Results presented in the paper demonstrate that the Twin
NN generalizes well and scales well on large unbalanced datasets.

8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int