@CONFERENCE\{IMM2010-05869,
    author       = "T. S. Alstr{\o}m and J. Larsen and C. H. Nielsen and N. B. Larsen",
    title        = "Data–driven modeling of nano-nose gas sensor arrays",
    year         = "2010",
    month        = "apr",
    keywords     = "Polymer Coated Quartz Crystal Microbalance Sensor (QCM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), Non–negative Matrix Factorization (NMF), Principal Component Analysis (PCA), Principal Component Regression (PCR)",
    pages        = "76970U",
    booktitle    = "Signal Processing, Sensor Fusion, and Target Recognition {XIX}",
    volume       = "7697",
    series       = "",
    editor       = "Ivan Kadar",
    publisher    = "The International Society for Optical Engineering",
    organization = "",
    address      = "",
    note         = "Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited",
    url          = "http://dx.doi.org/10.1117/12.850314",
    abstract     = "We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state-of-the-art machine learning methods and the Bayesian learning paradigm."
}