公开(公告)号：US20190253442A1

公开(公告)日：2019-08-15

申请号：US15895072

申请日：2018-02-13

Applicant: Cisco Technology, Inc.

Inventor： Jan Brabec ,  Lukas Machlica

IPC: H04L29/06 ,  G06N99/00

CPC classification number: H04L63/1425 ,  G06N20/00

Abstract: In one embodiment, a computing device trains a multi-class classifier (having a plurality of classes) on a training dataset, and evaluates the multi-class classifier on a testing dataset to determine a performance of each of the plurality of classes. The plurality of classes may then be partitioned into either learnable or unlearnable based on whether the performance each particular class surpasses a particular threshold, and then a predicting classifier can be trained on the training dataset, where data of the training dataset is labelled as either learnable or unlearnable based on the particular class to which the data corresponds. Accordingly, the computing device may then use the predicting classifier on a new class to predict whether samples associated with the new class are learnable or unlearnable, and may retrain the multi-class classifier with the samples associated with the new class in response to predicting that the samples are learnable.

公开(公告)号：US10356117B2

公开(公告)日：2019-07-16

申请号：US15648563

申请日：2017-07-13

Applicant: Cisco Technology, Inc.

Inventor： Jan Brabec ,  Lukas Machlica

IPC: G06F21/55 ,  G06N7/00 ,  H04L29/06 ,  G06N20/00

Abstract: In one embodiment, a computing device provides a feature vector as input to a random decision forest comprising a plurality of decision trees trained using a training dataset, each decision tree being configured to output a classification label prediction for the input feature vector. For each of the decision trees, the computing device determines a conditional probability of the decision tree based on a true classification label and the classification label prediction from the decision tree for the input feature vector. The computing device generates weightings for the classification label predictions from the decision trees based on the determined conditional probabilities. The computing device applies a final classification label to the feature vector based on the weightings for the classification label predictions from the decision trees.

公开(公告)号：US20190020670A1

公开(公告)日：2019-01-17

申请号：US15648563

申请日：2017-07-13

Applicant: Cisco Technology, Inc.

Inventor： Jan Brabec ,  Lukas Machlica

IPC: H04L29/06 ,  G06N99/00 ,  G06N7/00

CPC classification number: H04L63/1425 ,  G06N7/005 ,  G06N20/00 ,  H04L63/1458 ,  H04L69/22

Abstract: In one embodiment, a computing device provides a feature vector as input to a random decision forest comprising a plurality of decision trees trained using a training dataset, each decision tree being configured to output a classification label prediction for the input feature vector. For each of the decision trees, the computing device determines a conditional probability of the decision tree based on a true classification label and the classification label prediction from the decision tree for the input feature vector. The computing device generates weightings for the classification label predictions from the decision trees based on the determined conditional probabilities. The computing device applies a final classification label to the feature vector based on the weightings for the classification label predictions from the decision trees.