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Hybrid Transfer Active Learning for Multistream Processes With Within-Process and Cross-Process Correlation Modeling

Zhiyong Hu, Chao Wang

IEEE Transactions on Neural Networks and Learning Systems

|June 18, 2026

Summary

This study introduces a hybrid transfer learning framework to solve the cold-start problem in multistream active learning for regression (ALR). The method effectively models correlations within and across processes for improved functional relationship learning.

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Area of Science:

Machine Learning
Statistical Modeling

Background:

Active learning for regression (ALR) is crucial for learning functional relationships.
Existing ALR methods face challenges with the cold-start problem and single-stream limitations.

Purpose of the Study:

To propose a hybrid transfer learning framework for multistream ALR.
To address the cold-start problem by modeling within-process and cross-process functional correlations.

Main Methods:

Introduced a novel multioutput Gaussian process (MGP) covariance structure.
Integrated offline learning (cross-process knowledge transfer) and online updating (within-process refinement).

Main Results:

Demonstrated monotonic decrease in ALR error with data acquisition.
Showcased the framework's accuracy and superiority over benchmark methods.

Conclusions:

The proposed framework effectively resolves the cold-start problem in multistream ALR.
Hybrid transfer learning with MGP provides a robust approach for complex functional learning.