Efficient learning of pseudo-Boolean functions from limited training data
Pseudo-Boolean functions are generalizations of Boolean functions. We present a new method for learning pseudo-Boolean functions from limited training data. The objective of learning is to obtain a function f which is a good approximation of the target function f*. We define suitable criteria for the "goodness" of an approximating function. One criterion is to choose a function f that minimizes the "expected distance" with respect to a distance function d (over pairs of pseudoBoolean functions) and the uniform distribution over all feasible pseudo-Boolean functions. We define two alternative "distance measures" over pairs of pseudo-Boolean functions, and show that they are are actually equivalent with respect to the criterion of minimal expected distance. We outline efficient algorithms for learning pseudo-Boolean functions according to these criteria. Other reasonable distance measures and "goodness" criteria are also discussed. © Springer-Verlag Berlin Heidelberg 2005.
Publication Source (Journal or Book title)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Ding, G., Chen, J., Lax, R., & Chen, P. (2005). Efficient learning of pseudo-Boolean functions from limited training data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3488 LNAI, 323-331. https://doi.org/10.1007/11425274_34