相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Improving data and model quality in crowdsourcing using cross-entropy-based noise correction
Wenqiang Xu et al.
INFORMATION SCIENCES (2021)
Finding Multiple Roots of Nonlinear Equation Systems via a Repulsion-Based Adaptive Differential Evolution
Wenyin Gong et al.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2020)
Label similarity-based weighted soft majority voting and pairing for crowdsourcing
Fangna Tao et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2020)
Majority Voting and Pairing with Multiple Noisy Labeling
Victor S. Sheng et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2019)
Max-Margin Majority Voting for Learning from Crowds
Tian Tian et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)
Class-specific attribute weighted naive Bayes
Liangxiao Jiang et al.
PATTERN RECOGNITION (2019)
Noise correction to improve data and model quality for crowdsourcing
Chaoqun Li et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)
Computing Crowd Consensus with Partial Agreement
Nguyen Quoc Viet Hung et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2018)
Learning from multiple annotators using kernel alignment
J. Gil-Gonzalez et al.
PATTERN RECOGNITION LETTERS (2018)
Differential Evolution-Based Weighted Majority Voting for Crowdsourcing
Hao Zhang et al.
PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II (2018)
Using Differential Evolution to Estimate Labeler Quality for Crowdsourcing
Chen Qiu et al.
PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II (2018)
Multi-Label Answer Aggregation based on Joint Matrix Factorization
Jinzheng Tu et al.
2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) (2018)
Incorporating Worker Similarity for Label Aggregation in Crowdsourcing
Jiyi Li et al.
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT II (2018)
Multi-Label Inference for Crowdsourcing
Jing Zhang et al.
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)
A Quality-Sensitive Method for Learning from Crowds
Jinhong Zhong et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2017)
Learning from crowdsourced labeled data: a survey
Jing Zhang et al.
ARTIFICIAL INTELLIGENCE REVIEW (2016)
Deep feature weighting for naive Bayes and its application to text classification
Liangxiao Jiang et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2016)
Multi-Class Ground Truth Inference in Crowdsourcing with Clustering
Jing Zhang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2016)
Noise filtering to improve data and model quality for crowdsourcing
Chaoqun Li et al.
KNOWLEDGE-BASED SYSTEMS (2016)
Not always simple classification: Learning Super Parent for class probability estimation
Chen Qiu et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Imbalanced Multiple Noisy Labeling
Jing Zhang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)
Sequence labeling with multiple annotators
Filipe Rodrigues et al.
MACHINE LEARNING (2014)
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems
David R. Karger et al.
OPERATIONS RESEARCH (2014)
Parameter extraction of solar cell models using repaired adaptive differential evolution
Wenyin Gong et al.
SOLAR ENERGY (2013)
分享论文
