A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
The nature of statistical learning theory
The nature of statistical learning theory
Random walks and an O*(n5) volume algorithm for convex bodies
Random Structures & Algorithms
Discrete Applied Mathematics
ON-LINE LEARNING OF LINEAR FUNCTIONS
ON-LINE LEARNING OF LINEAR FUNCTIONS
The Journal of Machine Learning Research
Local soft belief updating for relational classification
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Supporting velocity of investigation with behavior analysis of malware
Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research
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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 pseudo-Boolean 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.