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Inhibition in multiclass classification
journal contribution
posted on 2023-06-08, 16:48 authored by Ramón Huerta, Shankar Vembu, José M Amigó, Thomas NowotnyThomas Nowotny, Charles ElkanThe 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.
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Publication status
- Published
File Version
- Published version
Journal
Neural ComputationISSN
0899-7667Publisher
MIT PressExternal DOI
Issue
9Volume
24Page range
2473-2507Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2014-03-04First Open Access (FOA) Date
2014-03-04First Compliant Deposit (FCD) Date
2014-03-03Usage metrics
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