Neural Comput - Inhibition in multiclass classification.

Tópicos

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Resumo

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.

Resumo Limpo

role inhibit investig multiclass support vector machin formal inspir brain structur insect socal mushroom bodi set output neuron classif function compet encod particular input strong activ output neuron depress inhibit remain output without know correct incorrect accord propos use classif function embodi unselect inhibit train larg margin classifi framework inhibit lead robust classifi sens perform better larger area appropri hyperparamet assess leaveoneout strategi also show classifi inhibit tight bound probabilist exponenti model bay consist class problem properti make approach use data set limit number label exampl larger data set signific compar advantag multiclass svm approach

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