Authors:
Moisés Laurence de Freitas Lima Junior
1
;
Will Ribamar Mendes Almeida
2
and
Areolino de Almeida Neto
3
Affiliations:
1
Department of Computing, Federal Institute of Tocantins (IFTO), Village Santa Teresa - km 06, Araguatins-TO and Brazil
;
2
Department of Computing, CEUMA University (UNICEUMA), Rua Josué Montello - 1, São Luis-MA and Brazil
;
3
MecaNET group, Federal University of Maranhão (UFMA), Av. dos Portugueses - 1966, São Luis-MA and Brazil
Keyword(s):
Deep Learning, Deep FeedForward, Deep Stacked Autoencoder.
Abstract:
The goal is an improvement on learning of deep neural networks. This improvement is here called the CollabNet network, which consists of a new method of insertion of new layers hidden in deep feedforward neural networks, changing the traditional way of stacking autoencoders. The new form of insertion is considered collaborative and seeks to improve the training against approaches based on stacked autoencoders. In this new approach, the addition of a new layer is carried out in a coordinated and gradual way, keeping under the control of the designer the influence of this new layer in training and no longer in a random and stochastic way as in the traditional stacking. The collaboration proposed in this work consists of making the learning of newly inserted layer continuing the learning obtained from previous layers, without prejudice to the global learning of the network. In this way, the freshly added layer collaborates with the previous layers and the set works in a way more aligned
to the learning. CollabNet has been tested in the Wisconsin Breast Cancer Dataset database, obtaining a satisfactory and promising result.
(More)