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What is Mean Square Error (MSE)

Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
It is used as the primary criterion to ascertain the extent of learning acquired by a learning system like ANN. It is defined as the average squared error between the network’s output and the target value over all the samples.
Published in Chapter:
Biometric Identification System Using Neuro and Fuzzy Computational Approaches
Tripti Rani Borah (Gauhati University, India), Kandarpa Kumar Sarma (Gauhati University, India), and Pranhari Talukdar (Gauhati University, India)
DOI: 10.4018/978-1-4666-8654-0.ch016
Abstract
In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.
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More Results
Learning Aided Digital Image Compression Technique for Medical Application
Is a cost functional used to ascertain the level of training that a prediction system achieves. MSE is to measure the closeness between present output to actual output.
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Swarm-Based Nature-Inspired Metaheuristics for Neural Network Optimization
Commonly used objective function to evaluate the performance of classification algorithms. It is defined as the variance of the estimator.
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