Publications [#322843] of Silvia Ferrari

Papers Published

1. Zhu, P; Wei, H; Lu, W; Ferrari, S, Multi-kernel probability distribution regressions, Proceedings of the International Joint Conference on Neural Networks, vol. 2015-September (September, 2015), IEEE, ISBN 9781479919604 [doi]
   (last updated on 2021/09/05)

   Abstract:
   This paper presents a multi-layer reproducing kernel Hilbert space (RKHS) approach for probability distribution to real and probability distribution to function regressions. The approach maps the distributions into RKHS by distribution embeddings and, then, constructs a multi-layer RKHS within which the multi-kernel distribution regression can be implemented using an existing kernel regression algorithm, such as kernel recursive least squares (KRLS). The numerical simulations on synthetic data obtained via Gaussian mixtures show that the proposed approach outperforms existing probability distribution (DR) regression algorithms by achieving smaller mean squared errors (MSEs) and requiring less training samples.