Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique

Chelsia Amy Doukim, and Jamal Ahmad Dargham, and Chekima, Ali (2010) Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique. In: 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA 2010), 10-13 May 2010, Kuala Lumpur, Malaysia.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/ISSPA.2010.5605430

Abstract

Skin detection is an important preliminary process for subsequent feature extraction in image processing techniques. There are several techniques that are used for skin detection. In this work, the multi-layer perceptron (MLP) neural network is used. One of the important aspects of MLP is how to determine the network topology. The number of neurons in the inputs and output layers are determined by the number of available inputs and required outputs respectively. Thus, the only thing remaining is how to determine the number of neurons in the hidden layer. Therefore, we employed the coarse to fine search method to find the number of neurons. First, the number of hidden neurons is initially set using the binary search mode, HN=1, 2, 4, 8, 16, 32, 64 and 128, where HN indicates the number of hidden neurons. The 30 networks with these HN values are trained and their Mean Squared Error (MSE) is calculated. Then a sequential search, fine search, will be used in the neighbourhood of the HN that gave the lowest MSE. The selected number of neurons in the hidden layer is the lowest HN that gave the lowest MSE. The YCbCr colour space is used in this work due to its capability to separate the luminance and chrominance components explicitly. Several chrominance components are investigated. © 2010 IEEE.

Item Type:Conference Paper (UNSPECIFIED)
Uncontrolled Keywords:Feature extraction, Multi-layer perceptron, Skin detection, Binary search, Coarse to fine, Colour spaces, Hidden layers, Hidden neurons, Image processing technique, Mean squared error, MLP neural networks, Neighbourhood, Network topology, Output layer, Search method, Search technique, Sequential search
Subjects:?? TR624-835 ??
Divisions:SCHOOL > School of Engineering and Information Technology
ID Code:2163
Deposited By:IR Admin
Deposited On:10 Mar 2011 13:17
Last Modified:30 Dec 2014 09:31

Repository Staff Only: item control page


Browse Repository
Collection
   Articles
   Book
   Speeches
   Thesis
   UMS News
Search
Quick Search

   Latest Repository

Link to other Malaysia University Institutional Repository

Malaysia University Institutional Repository