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Inhibition in multiclass classification

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posted on 2023-06-08, 16:48 authored by Ramón Huerta, Shankar Vembu, José M Amigó, Thomas NowotnyThomas Nowotny, Charles Elkan
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Neural Computation

ISSN

0899-7667

Publisher

MIT Press

Issue

9

Volume

24

Page range

2473-2507

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2014-03-04

First Open Access (FOA) Date

2014-03-04

First Compliant Deposit (FCD) Date

2014-03-03

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