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Computed Decision Weights and a New Learning Algorithm for Neural Classifiers

arXiv 2209.08422 ↗First posted 17 Sep 2022cs.LG stat.MLcited by 0

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Abstract (arXiv)

In this paper we consider the possibility of computing rather than training the decision layer weights of a neural classifier. Such a possibility arises in two way, from making an appropriate choice of loss function and by solving a problem of constrained optimization. The latter formulation leads to a promising new learning process for pre-decision weights with both simplicity and efficacy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 5 references that are in the map. First posted on arXiv 17 Sep 2022.

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References · 2

  1. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
  2. 2019Class Mean Vectors, Self Monitoring and Self Learning for Neural Classifierscited by 1

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