US 12,242,520 B2
Training a multi-label classifier
Ravindra K. Balupari, San Jose, CA (US); and Sandeep Yadav, South San Francisco, CA (US)
Assigned to Netskope, Inc., Santa Clara, CA (US)
Filed by Netskope, Inc., Santa Clara, CA (US)
Filed on Sep. 28, 2023, as Appl. No. 18/476,484.
Application 18/476,484 is a continuation of application No. 17/396,503, filed on Aug. 6, 2021, granted, now 11,809,467.
Application 17/396,503 is a continuation of application No. 16/226,394, filed on Dec. 19, 2018, granted, now 11,087,179, issued on Aug. 10, 2021.
Prior Publication US 2024/0028625 A1, Jan. 25, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/31 (2019.01); G06F 16/35 (2019.01); G06F 16/951 (2019.01); G06F 18/2411 (2023.01); G06N 20/10 (2019.01)
CPC G06F 16/313 (2019.01) [G06F 16/35 (2019.01); G06F 16/951 (2019.01); G06F 18/2411 (2023.01); G06N 20/10 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
training a multi-label classifier, the training comprising:
accessing training examples for documents belonging to a plurality of label classes, wherein a number of the plurality of label classes is more than fifty (50) label classes;
creating document features representing aspects of words in each of the documents;
training a support vector machine with the document features for one-vs-the-rest classification using the plurality of label classes, the training comprising:
training a one-vs-the-rest classifier the number of times such that the one-vs-the-rest classifier is trained one time for each of the plurality of label classes, each training including:
providing the training examples belonging to the respective label class to the support vector machine running the one-vs-the-rest classifier to obtain training output labels, and
comparing the training output labels using a linear support vector machine classifier to generate the number of hyperplane determinations that separate each label class of the plurality of label classes from the rest of the plurality of label classes; and
storing parameters of the trained support vector machine, the parameters comprising the hyperplane determinations.