Publications

Authors:

Squarcina, Letizia; Castellani, Umberto; Brambilla, Paolo

Title:

Multiple kernel learning

Year:

2020

Type of item:

Contributo in volume (Capitolo o Saggio)

Tipologia ANVUR:

Contributo in volume (Capitolo o Saggio)

Language:

Inglese

Format:

A Stampa

Book Title:

Machine Learning Methods and Applications to Brain Disorders

Publisher:

Academic Press

ISBN:

9780128157398

Page numbers:

141-156

Keyword:

Brain classification; Multiple Kernel Learning; Support Vectore Machines; Psycosis

Short description of contents:

The use of kernels in machine learning methods allows the identification of an optimal hyperplane for the separation of two classes (e.g., patients with a brain disorder of interest and healthy controls). When different acquisition modalities or different types of data are available, using a single kernel for all the available data is a disadvantage because data heterogeneity cannot be fully exploited. Multiple kernel learning (MKL) addresses this issue by allowing the integration of different kernels within the same machine learning model. It has been demonstrated that MKL methods outperform single kernel methods when applied to brain disorders data, while at the same time making it possible to understand which features (e.g., brain region) are the most informative for classification. In this chapter, we provide an overview of MKL methods and illustrate their potential by reviewing recent applications to Alzheimer's disease, Parkinson's disease, and psychosis.

Web page:

https://www.sciencedirect.com/science/article/pii/B9780128157398000080

Product ID:

114048

Handle IRIS:

11562/1016256

Last Modified:

November 21, 2024

Bibliographic citation:

Squarcina, Letizia; Castellani, Umberto; Brambilla, Paolo, Multiple kernel learning Machine Learning Methods and Applications to Brain Disorders , Academic Press , 2020 , pp. 141-156

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