NDLI: A general insight into the effect of neuron structure on classification

Content Provider	Springer Nature Link
Author	Sadoghi Yazdi, Hadi Rowhanimanesh, Alireza Modares, Hamidreza
Copyright Year	2011
Abstract	This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.
Starting Page	135
Ending Page	154
Page Count	20
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	30
Issue Number	1
e-ISSN	02193116
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-03-31
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neuron structure Nonmonotonic activation function Nonlinear input mapping Classification Multilayer perceptron (MLP) Iris data classification Business Information Systems Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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