Skip to main navigation
Skip to search
Skip to main content
Hong Kong Baptist University Home
Help & FAQ
Link opens in a new tab
Search content at Hong Kong Baptist University
Home
Scholars
Departments / Units
Research Output
Projects / Grants
Prizes / Awards
Activities
Student theses
Datasets
Learning and Mining with Noisy Labels
Masashi Sugiyama
, Tongliang Liu
,
Bo Han
, Yang Liu
, Gang Niu
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
3
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Learning and Mining with Noisy Labels'. Together they form a unique fingerprint.
Sort by:
Weight
Alphabetically
Keyphrases
Noisy Labels
100%
Machine Learning
42%
Deep Learning
42%
Statistical Learning
28%
Developing Countries
28%
Noisy Data
28%
Asia
14%
Existing Algorithms
14%
Image Data
14%
Learning Algorithm
14%
Deep Network
14%
Unbiased Risk Estimation
14%
Dataset Size
14%
Africa
14%
Data Mining Applications
14%
Web-scale
14%
Senate
14%
Machine Learning Datasets
14%
Statistical Learning Theory
14%
Knowledge Management
14%
Supervised Information
14%
Memorization
14%
Data with Imperfect Labels
14%
Loss Risk
14%
Risk Estimator
14%
Speech Data
14%
Noisy Supervision
14%
Deep-sea Mining
14%
Shallow Learning
14%
Data Harms
14%
Mining Management
14%
Noise-tolerant Loss
14%
Engineering
Learning System
100%
Deep Learning Method
75%
Developing Country
50%
Noisy Data
50%
Risk Estimator
50%
Learning Algorithm
25%
Image Data
25%
Scale Image
25%
Statistical Learning Theory
25%
Speech Data
25%
Knowledge Management
25%
Computer Science
Machine Learning
100%
Learning System
100%
Deep Learning Method
75%
Risk Estimator
50%
Learning Algorithm
25%
Data Mining
25%
Data Mining Application
25%
Statistical Learning Theory
25%
Knowledge Management
25%
Supervised Information
25%
Psychology
Learning Algorithm
100%