Late Breaking Results: Hyperdimensional Regression with Fine-Grained and Scalable Confidence-Based Learning

We propose an advanced hyperdimensional computing (HDC) framework for regression tasks, addressing the limitations of existing methods through three key innovations: fine-grained feature encoding, confidence-based inference, and dimension-split boosting for scalable training. By preserving inter-feature relationships and enabling efficient computation on high-dimensional spaces, the framework achieves superior accuracy and efficiency across diverse benchmarks. Our evaluation demon-strates that HB R F achieves significant improvements in prediction quality and computational efficiency as compared to the state-of-the-art HDC- based regression by 31% and 54.8 %, respectively.