The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 16, 2024

Filed:

Nov. 17, 2020
Applicants:

Robert Bosch Gmbh, Stuttgart, DE;

Carnegie Mellon University, Pittsburgh, PA (US);

Inventors:

Rizal Fathony, Pittsburgh, PA (US);

Frank Schmidt, Leonberg, DE;

Jeremy Zieg Kolter, Pittsburgh, PA (US);

Assignees:

ROBERT BOSCH GMBH, Stuttgart, DE;

CARNEGIE MELLON UNIVERSITY, Pittsburgh, PA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/0464 (2023.01); A63F 13/67 (2014.01); G05B 19/4155 (2006.01); G06N 3/08 (2023.01); G06N 3/084 (2023.01); G07C 3/00 (2006.01); G07C 9/10 (2020.01); G06F 18/21 (2023.01); G06F 18/24 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); A63F 13/67 (2014.09); G05B 19/4155 (2013.01); G06N 3/0464 (2023.01); G06N 3/084 (2013.01); G07C 3/005 (2013.01); G07C 9/10 (2020.01); G05B 2219/40496 (2013.01); G06F 18/21 (2023.01); G06F 18/24 (2023.01);
Abstract

A computer-implemented method for training a classifier, particularly a binary classifier, for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier. The method includes providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors.


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