Huerta, Ramón, Vembu, Shankar, Amigó, José M, Nowotny, Thomas and Elkan, Charles (2012) Inhibition in multiclass classification. Neural Computation, 24 (9). pp. 2473-2507. ISSN 0899-7667

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Abstract

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions,
that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a
classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems.
These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches.

Item Type:	Article
Schools and Departments:	School of Engineering and Informatics > Informatics
Subjects:	Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Related URLs:	Publisher
Depositing User:	Thomas Nowotny
Date Deposited:	04 Mar 2014 09:19
Last Modified:	07 Mar 2017 10:38
URI:	http://sro.sussex.ac.uk/id/eprint/47665

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