Active learning support vector machines

Active Learning Support Vector Machines for Optimal Sample Selection in Classification. 

Warmuth, M.K., Liao, )., Ratsch, G., Mathieson, M., and Purr A, S. Active learning with support vector machines in the drug discovery process./. Chem. Inf. Comput. Sci. 2003, 43, 667-673. 

Active Learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 43, 667-673. 

Todeschini R, Consonni V (2000) Handbook of molecular descriptors. In Mannhold R, Kubinyi H, Timmerman H (eds), Methods and principles in medicinal chemistry 11. WILEY-VCH, Weinheim. Walters WP, Stahl MT, Murcko MA (1998) Virtual screening — and overview. Drug Discov Today 3 160—178. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58 236—244. Warmuth MK, Liao J, Ratsch G et al. (2003) Active learning with support vector machines in the drug discovery process. J Chem Inf Comput Sci 43 667—673. 

Abstract. Artificial neural networks (ANN) are useful components in today s data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits. 

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