Elements of Computational Learning Theory

Ke-Lin Du () and M. N. S. Swamy
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Ke-Lin Du: Concordia University, Department of Electrical and Computer Engineering
M. N. S. Swamy: Concordia University, Department of Electrical and Computer Engineering

Chapter Chapter 3 in Neural Networks and Statistical Learning, 2019, pp 65-79 from Springer

Abstract: Abstract PAC learning theory is the foundation of computational learning theory. VC-dimension, Rademacher complexity, and empirical risk-minimization principle are three concepts for deriving a generalization error bound for a trained machine. The fundamental theorem of learning theory relates PAC learnability, VC-dimension, and empirical risk-minimization principle. Another basic theorem in computational learning theory is no-free-lunch theorem. These topics are addressed in this chapter.

Date: 2019
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DOI: 10.1007/978-1-4471-7452-3_3

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