Incremental Regression: LWPR

This lecture covers incremental learning, focusing on the LWPR algorithm. It discusses the challenges of updating previous models with new data, the concept of synthetic data generation, and the importance of incremental learning in various real-world applications such as robot learning, speech processing, and finances. The LWPR algorithm, inspired by Locally Weighted Regression, is explained in detail, emphasizing its use of receptive fields and RBF kernels to compute regressive lines. The lecture also delves into the basic incremental algorithm of LWPR, including the learning process for distance metrics and the creation of new models based on query points.