Welcome to BBF’s documentation!

BBF [1] is an fine-tuning approach that bases on boosting [2] . It is designed to be efficient with the following advantages:

• Support of fine-tuning an initial model for classification.

• Support of fine-tuning an initial model for regression.

Contents:

• Python Quick Start
• Parameters Tuning
• Python API
• Python Examples

References

[1] C. Zhao, R. Peng and D. Wu, “Bagging and Boosting Fine-tuning (BBF) for Ensemble Learning,” IEEE Trans. on Neural Networks and Learning Systems, submitted, 2022.

[2] J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.

Indices and tables

• Index