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FOGA
1990
13 years 8 months ago
A Hierarchical Approach to Learning the Boolean Multiplexer Function
This paper describes the recently developed genetic programming paradigm which genetically breeds populations of computer programs to solve problems. In genetic programming, the i...
John R. Koza
GECCO
2005
Springer
117views Optimization» more  GECCO 2005»
14 years 1 months ago
Extending XCSF beyond linear approximation
XCSF is the extension of XCS in which classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. XCSF can...
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...
IACR
2011
115views more  IACR 2011»
12 years 7 months ago
Pseudorandom Functions and Lattices
We give direct constructions of pseudorandom function (PRF) families based on conjectured hard lattice problems and learning problems. Our constructions are asymptotically effici...
Abhishek Banerjee, Chris Peikert, Alon Rosen
ICALP
2001
Springer
14 years 9 hour ago
Separating Quantum and Classical Learning
We consider a model of learning Boolean functions from quantum membership queries. This model was studied in [26], where it was shown that any class of Boolean functions which is i...
Rocco A. Servedio
FOCS
1989
IEEE
13 years 11 months ago
Constant Depth Circuits, Fourier Transform, and Learnability
In this paper, Boolean functions in ,4C0 are studied using harmonic analysis on the cube. The main result is that an ACO Boolean function has almost all of its “power spectrum”...
Nathan Linial, Yishay Mansour, Noam Nisan