公开(公告)号：US20200250527A1

公开(公告)日：2020-08-06

申请号：US16750053

申请日：2020-01-23

Applicant: Google LLC

Inventor： Qi Zhao ,  Abbas Kazerouni ,  Sandeep Tata ,  Jing Xie ,  Marc Najork

IPC: G06N3/08 ,  G06N3/04

Abstract: The present disclosure provides computing systems and methods directed to active learning and may provide advantages or improvements to active learning applications for skewed data sets. A challenge in training and developing high-quality models for many supervised learning scenarios is obtaining labeled training examples. This disclosure provides systems and methods for active learning on a training dataset that includes both labeled and unlabeled datapoints. In particular, the systems and methods described herein can select (e.g., at each of a number of iterations) a number of the unlabeled datapoints for which labels should be obtained to gain additional labeled datapoints on which to train a machine-learned model (e.g., machine-learned classifier model). Generally, the disclosure provides cost-effective methods and systems for selecting data to improve machine-learned models in applications such as the identification of content items in text, images, and/or audio.

公开(公告)号：US11526752B2

公开(公告)日：2022-12-13

申请号：US16750053

申请日：2020-01-23

Applicant: Google LLC

Inventor： Qi Zhao ,  Abbas Kazerouni ,  Sandeep Tata ,  Jing Xie ,  Marc Najork

IPC: G06N3/08 ,  G06N3/04

Abstract: Provided are computing systems and methods directed to active learning and may provide advantages or improvements to active learning applications for skewed data sets. A challenge in training and developing high-quality models for many supervised learning scenarios is obtaining labeled training examples. Provided are systems and methods for active learning on a training dataset that includes both labeled and unlabeled datapoints. In particular, the systems and methods described herein can select (e.g., at each of a number of iterations) a number of the unlabeled datapoints for which labels should be obtained to gain additional labeled datapoints on which to train a machine-learned model (e.g., machine-learned classifier model). Generally, provided are cost-effective methods and systems for selecting data to improve machine-learned models in applications such as the identification of content items in text, images, and/or audio.