Abstract
We present a survey of recent results concerning the theoretical and empirical performance of algorithms for learning regularized least-squares classifiers. The behavior of these family of learning algorithms is analyzed in both the statistical and the worst-case (individual sequence) data-generating models.
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Cesa-Bianchi, N. (2004). Applications of Regularized Least Squares to Classification Problems. In: Ben-David, S., Case, J., Maruoka, A. (eds) Algorithmic Learning Theory. ALT 2004. Lecture Notes in Computer Science(), vol 3244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30215-5_2
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DOI: https://doi.org/10.1007/978-3-540-30215-5_2
Publisher Name: Springer, Berlin, Heidelberg
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