LGDec 22, 2017
Learning the Kernel for Classification and Regression
arXiv:1712.08597v23 citations
Originality Synthesis-oriented
AI Analysis
This work addresses kernel selection for machine learning practitioners, but appears incremental as it builds on existing kernel methods.
The paper tackles regression and classification problems by learning polynomial combinations of base kernels, and reports results from numerical experiments on various datasets.
We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems. We also perform some numerical experiments of polynomial kernels with regression and classification tasks on different datasets.