Work

Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Year: 2011

Type: preprint

Abstract: We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the marginal likelihood and contains many regularised risk approaches as special cases. Furthermore, we derive an efficient and provably convergent optimisation algorithm.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1103.0897

Authors: Nickisch, Hannes, Seeger, Matthias

Institutions: Max Planck Society, Max Planck Institute for Intelligent Systems, École Polytechnique Fédérale de Lausanne

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2103499365

Cites: 16

Cited by: 8

Related to: 10