Algorithms 2008, 1(2), 130-152; doi:10.3390/a1020130

Machine Learning: A Crucial Tool for Sensor Design

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Received: 31 October 2008; in revised form: 21 November 2008 / Accepted: 29 November 2008 / Published: 3 December 2008
(This article belongs to the Special Issue Sensor Algorithms)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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.
Keywords: Sensors; Machine Learning; Feature Extraction; Multivariate Analysis; Neural Networks; Optimization
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MDPI and ACS Style

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.

AMA Style

Zhao W, Bhushan A, Santamaria AD, Simon MG, Davis CE. Machine Learning: A Crucial Tool for Sensor Design. Algorithms. 2008; 1(2):130-152.

Chicago/Turabian Style

Zhao, Weixiang; Bhushan, Abhinav; Santamaria, Anthony D.; Simon, Melinda G.; Davis, Cristina E. 2008. "Machine Learning: A Crucial Tool for Sensor Design." Algorithms 1, no. 2: 130-152.

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