Heterogeneous Multilayer Generalized Operational Perceptron
Author | Tran D.T. |
Author | Kiranyaz, Mustafa Serkan |
Author | Gabbouj M. |
Author | Iosifidis A. |
Available date | 2022-04-26T12:31:20Z |
Publication Date | 2020 |
Publication Name | IEEE Transactions on Neural Networks and Learning Systems |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/TNNLS.2019.2914082 |
Abstract | The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron (GOP) was proposed to extend the conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, a progressive operational perceptron (POP) algorithm was proposed to optimize a predefined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Learning systems Multilayers Architecture learning Feed-forward network generalized operational perceptron (GOP) Mcculloch-pitts neuron models Multi layer perceptron Multi-layer network Nonlinear thresholding Progressive learning Neurons algorithm classification factual database Algorithms Databases, Factual Neural Networks, Computer |
Type | Article |
Pagination | 710-724 |
Issue Number | 3 |
Volume Number | 31 |
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