公开(公告)号：US20240119341A1

公开(公告)日：2024-04-11

申请号：US17953255

申请日：2022-09-26

Applicant: Lemon Inc.

Inventor： Xin YANG ,  Hanlin ZHU ,  Tianyi LIU ,  Jiankai SUN ,  Yuanshun YAO ,  Aonan ZHANG ,  Chong WANG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The present disclosure describes techniques for determining performance of a classifier. A first machine learning model and a second machine learning model may be trained by aggregating updates to the first machine learning model and the second machine learning model received from a plurality of client computing devices. A cumulative distribution function (CDF) associated with a distribution of the positive samples in the user data may be estimated using the trained first machine learning model. A probability density function (PDF) associated with a distribution of the negative samples in the user data may be estimated using the trained second machine learning model. An integration-based computation of an area under the receiver operating characteristic curve (AUC) of the classifier may be performed using the PDF and the CDF.

公开(公告)号：US20230267363A1

公开(公告)日：2023-08-24

申请号：US17666076

申请日：2022-02-07

Applicant: Lemon Inc.

Inventor： Yingxiang YANG ,  Tianyi LIU ,  Taiqing WANG ,  Chong WANG ,  Zhihan XIONG

IPC: G06N20/00 ,  G06F17/14

CPC classification number: G06N20/00 ,  G06F17/14

Abstract: Embodiments of the present disclosure relate to machine learning with periodic data. According to embodiments of the present disclosure, a feature representation of an input data sample is obtained from a prediction model. First Fourier coefficients for a first component in a Fourier expansion are determined by applying the feature representation into a first mapping model, and second Fourier coefficients for a second component in the Fourier expansion are determined by applying the feature representation into a second mapping model. A Fourier expansion result is determined based on the first Fourier coefficients and the second Fourier coefficients in the Fourier expansion, and a prediction result for the input data sample is determined based on the Fourier expansion result.