Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DEVDAN: Deep Evolving Denoising Autoencoder   The Denoising Autoencoder (DAE) enhances the flexibility of the data stream
method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for
data stream analytic deserves an in-depth study because it characterizes a
fixed network capacity that cannot adapt to rapidly changing environments. Deep
evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features
an open structure in the generative phase and the discriminative phase where
the hidden units can be automatically added and discarded on the fly. The
generative phase refines the predictive performance of the discriminative model
exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific
threshold and works fully in the single-pass learning fashion. We show that
DEVDAN can find competitive network architecture compared with state-of-the-art
methods on the classification task using ten prominent datasets simulated under
the prequential test-then-train protocol.

27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph