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Machine Learning: A Crucial Tool for Sensor Design

Department of Mechanical and Aeronautical Engineering, One Shields Avenue, University of California, Davis, CA 95616, USA
Author to whom correspondence should be addressed.
Algorithms 2008, 1(2), 130-152;
Received: 31 October 2008 / Revised: 21 November 2008 / Accepted: 29 November 2008 / Published: 3 December 2008
(This article belongs to the Special Issue Sensor Algorithms)
PDF [354 KB, uploaded 3 December 2008]


Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies. View Full-Text
Keywords: Sensors; Machine Learning; Feature Extraction; Multivariate Analysis; Neural Networks; Optimization Sensors; Machine Learning; Feature Extraction; Multivariate Analysis; Neural Networks; Optimization

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhao, W.; Bhushan, A.; Santamaria, A.D.; Simon, M.G.; Davis, C.E. Machine Learning: A Crucial Tool for Sensor Design. Algorithms 2008, 1, 130-152.

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