3 papers
cs.LG2026
Negative Ontology of True Target for Machine Learning: Towards Recognition, Evaluation and Learning under Democratic Supervision
Yongquan Yang
This article philosophically examines how a shift in the assumed ontology of the true target (TT) can lead to a new paradigm for machine learning (ML)-based predictive modelling. B…
cs.LG2026
LAF-Based Evaluation and UTTL-Based Learning Strategies with MIATTs
Yongquan Yang
In many real-world machine learning (ML) applications, the true target cannot be precisely defined due to ambiguity or subjectivity information. To address this challenge, under th…
q-bio.QM2025
Experts' cognition-driven safe noisy labels learning for precise segmentation of residual tumor in breast cancer
Yongquan Yang, Jie Chen, Yani Wei +2
Precise segmentation of residual tumor in breast cancer (PSRTBC) after neoadjuvant chemotherapy is a fundamental key technique in the treatment process of breast cancer. However, a…