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. Keywords Type Conference paper [With referee] Conference Signal Processing, Sensor Fusion, and Target Recognition XIX Editors Ivan Kadar Year 2010    Month April    Vol. 7697    No. 1    pp. 76970U Publisher The International Society for Optical Engineering 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 Electronic version(s) [pdf] Publication link http://dx.doi.org/10.1117/12.850314 BibTeX data [bibtex] IMM Group(s) Intelligent Signal Processing