Dec 5, 2020 · There is another use case of monotone classification – this is the task of combining classifiers of a general nature, also called base ...

Dec 5, 2020 · We consider a binary classification problem in which the class label is given in the form of a discriminant function that satisfies a monotone constraint.

This manuscript examines how such a discriminant function can be trained on the basis of a labeled data set. Two alternative quality measures are considered.

This manuscript examines how such a discriminant function can be trained on the basis of a labeled data set. Two alternative quality measures are considered.

Sergey Grosman: Trainable monotone combiner. Neurocomputing 417: 86-105 (2020). manage site settings. To protect your privacy, all features that rely on ...

Research paper thumbnail of Trainable monotone combiner · Trainable monotone combiner. Neurocomputing, 2020. We consider a binary classification problem in ...

Jun 16, 2020 · First, we present a counterexample-guided algorithm that provably guarantees monotonicity at prediction time, given an arbitrary ReLU neural ...

Trainable monotone combiner. Sergey Grosman. Journal: Neurocomputing, 2020, Volume 417, Page 86. DOI: 10.1016/j.neucom.2020.07.075 · Read Online · 16citations.

Trainable monotone combiner · S. Grosman. Computer Science, Mathematics. Neurocomputing. 2020. Add to Library. Alert. Explaining Adverse Actions in Credit ...

In this paper, we consider monotone multi-index models. Namely, the transfer function is assumed to be coordinate-wise monotone. The monotone multi-index model ...