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AuthorTran D.T.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
AuthorIosifidis A.
Available date2022-04-26T12:31:20Z
Publication Date2020
Publication NameIEEE Transactions on Neural Networks and Learning Systems
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TNNLS.2019.2914082
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081242263&doi=10.1109%2fTNNLS.2019.2914082&partnerID=40&md5=b238251898daf81e7a3d6c57c579dbb5
URIhttp://hdl.handle.net/10576/30609
AbstractThe 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.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectLearning 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
TitleHeterogeneous Multilayer Generalized Operational Perceptron
TypeArticle
Pagination710-724
Issue Number3
Volume Number31
dc.accessType Abstract Only


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