@inproceedings{41618fade245447a831e627711f440db,

title = "ℓ Regularization in infinite dimensional feature spaces",

abstract = "In this paper we discuss the problem of fitting ℓ1 regularized prediction models in infinite (possibly non-countable) dimensional feature spaces. Our main contributions are: a. Deriving a generalization of ℓ1 regularization based on measures which can be applied in non-countable feature spaces; b. Proving that the sparsity property of ℓ1 regularization is maintained in infinite dimensions; c. Devising a path-following algorithm that can generate the set of regularized solutions in {"}nice{"} feature spaces; and d. Presenting an example of penalized spline models where this path following algorithm is computationally feasible, and gives encouraging empirical results.",

author = "Sanaron Rosset and Grzegorz Swirszcz and Nathan Srebro and Ji Zhu",

year = "2007",

language = "אנגלית",

isbn = "9783540729259",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "544--558",

booktitle = "Learning Theory - 20th Annual Conference on Learning Theory, COLT 2007, Proceedings",

note = "null ; Conference date: 13-06-2007 Through 15-06-2007",

}