Generating a training model based on feedback

Patent metadata

Field	Value
Publication number	US-9659258-B2
Application number	US-201314025208-A
Country	US
Kind code	B2
Filing date	Sep 12, 2013
Priority date	Sep 12, 2013
Publication date	May 23, 2017
Grant date	May 23, 2017

Abstract

A method and apparatus for generating a training model based on feedback are provided. The method for generating a training model based on feedback, includes calculating an eigenvector of a sample among a plurality of samples; obtaining scores granted by a user for one or more of the plurality of samples in a round, obtaining scores granted by the user for a first number of samples; obtaining scores granted by the user for a second number of samples in response to detecting, based on the eigenvector, an inconsistency between the scores granted by the user for the first number of samples; and generating a training model based on the scores granted by the user for the first and second numbers of samples. A corresponding apparatus is also provided.

First claim

The invention claimed is:

1. A method for generating a training model based on feedback, comprising:
calculating an eigenvector of an image data sample among a plurality of image data samples;
obtaining total scores granted by at least one user for at least one of the plurality of image data samples in at least one round, comprising:
obtaining first sample scores granted by the at least one user for a first number of image data samples;
displaying a second number of image data samples to the at least one user for obtaining second sample scores granted by the at least one user for the second number of image data samples in response to having detected, based on the eigenvector, an inconsistency in the first sample scores; and

generating a training model stored in non-transitory computer readable storage media based on the total scores granted by the at least one user.

2. The method according to claim 1 , further comprising:
clustering the plurality of image data samples into at least one group based on the eigenvector.

3. The method according to claim 2 , wherein at least one of the first sample scores and the second sample scores comprises:
obtaining a score granted by the at least one user for at least one image data sample in each group of the at least one group.

4. The method according to claim 1 , wherein at least one of the first sample scores and the second sample scores, comprises: for one user among the at least one user,
providing at least two image data samples;
obtaining original scores granted by the user for the at least two image data samples; and
normalizing the original scores to obtain scores granted by the user for the at least two image data samples.

5. The method according to claim 1 , wherein obtaining the second sample scores, comprises:
if a difference between scores granted by a first user among the at least one user for similar image data samples exceeds a first threshold, providing more similar image data samples to the first user to obtain scores granted by the first user for the more similar image data samples.

6. The method according to claim 1 , wherein the obtaining the second sample scores, comprises:
if a difference between scores granted by similar users for a first image data sample of the plurality of image data samples exceeds a second threshold, providing the first image data sample once again to the similar users respectively to obtain scores granted by the similar users for the first image data sample.

7. The method according to claim 1 , wherein the obtaining the second sample scores, comprises:
if a gap between a difference between original scores granted by one user among the at least one user for two image data samples and a difference between normalized scores exceeds a third threshold, substituting one of the two image data samples with another image data sample of the plurality of image data samples.

8. The method according to claim 1 , wherein the generating a training model based on the total scores granted by the at least one user, comprises:
filling a scoring matrix with the total scores granted by the at least one user; and
collaboratively filtering the scoring matrix to generate the training model.

Assignees

• IBM

Inventors

• Cao Liangliang
• Duan Ning
• Lin Qian
• Wang Chen
• Yan Junchi
• Zhang Xin

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• G06N5/025

  Extracting rules from data · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06N99/005 Primary

  Physics · mapped topic

• G06N7/023

  Learning or tuning the parameters of a fuzzy system · CPC title