公开(公告)号：US11169964B2

公开(公告)日：2021-11-09

申请号：US16073961

申请日：2015-12-11

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kave Eshghi ,  Omar Aguilar Macedo

IPC: G06F7/00 ,  G06F16/13

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

公开(公告)号：US20170316340A1

公开(公告)日：2017-11-02

申请号：US15142798

申请日：2016-04-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kave Eshghi

IPC: G06N99/00

CPC classification number: G06N20/00

Abstract: In some examples, a system includes an access engine and a hyperplane determination engine. The access engine may access a training vector set that includes sparse binary training vectors and a set of labels classifying each of the sparse binary training vectors through a positive label or a negative label. The hyperplane determination engine may initialize a candidate hyperplane vector and maintain a scoring vector including scoring vector elements to track separation variances of the sparse binary training vectors with respect to the candidate hyperplane vector. Through iterations of identifying, according to the scoring vector, a particular sparse binary training vector with a greatest separation variance with respect to the candidate hyperplane vector, the hyperplane determination engine may incrementally update the candidate hyperplane vector and incrementally update the scoring vector to adjust separation variances affected by updates to the candidate hyperplane vector.