title: Spectral algorithms for supervised learning.
abstract: We discuss how a large class of regularization methods, collectively known as spectral regularization and originally designed for solving ill-posed inverse problems, gives rise to regularized learning algorithms. All of these algorithms are consistent kernel methods that can be easily implemented. The intuition behind their derivation is that the same principle allowing for the numerical stabilization of a matrix inversion problem is crucial to avoid overfitting. The various methods have a common derivation but different computational and theoretical properties. We describe examples of such algorithms, analyze their classification performance on several data sets and discuss their applicability to real-world problems.
authors.affiliation: Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, 16146 Genoa, Italy. logerfo@disi.unige.it
authors.forename: L
authors.initials: L
authors.lastname: Lo Gerfo
authors.forename: L
authors.initials: L
authors.lastname: Rosasco
authors.forename: F
authors.initials: F
authors.lastname: Odone
authors.forename: E
authors.initials: E
authors.lastname: De Vito
authors.forename: A
authors.initials: A
authors.lastname: Verri
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019992:Databases as Topic; D016018:Least-Squares Analysis; D012044:Regression Analysis; D013997:Time Factors