Abstract
In this chapter we apply the CE method to several problems arising in machine learning, specifically with respect to optimization. In Section 8.1, adapted from [50], we apply CE to the well-known mastermind game. Section 8.2, based partly on [112], describes the application of the CE method to Markov decision processes. Finally, in Section 8.3 the CE method is applied to clustering problems. In addition to its simplicity, the advantage of using the CE method for machine learning is that it does not require direct estimation of the gradients, as many other algorithms do (for example, the stochastic approximation, steepest ascent, or conjugate gradient method). Moreover, as a global optimization procedure the CE method is quite robust with respect to starting conditions and sampling errors, in contrast to some other heuristics, such as simulated annealing or guided local search.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer Science+Business Media New York
About this chapter
Cite this chapter
Rubinstein, R.Y., Kroese, D.P. (2004). Applications of CE to Machine Learning. In: The Cross-Entropy Method. Information Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-4321-0_8
Download citation
DOI: https://doi.org/10.1007/978-1-4757-4321-0_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-1940-3
Online ISBN: 978-1-4757-4321-0
eBook Packages: Springer Book Archive