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TEC
2008
115views more  TEC 2008»
15 years 3 months ago
Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction
An important strength of learning classifier systems (LCSs) lies in the combination of genetic optimization techniques with gradient-based approximation techniques. The chosen app...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
CSR
2011
Springer
14 years 6 months ago
The Complexity of Inversion of Explicit Goldreich's Function by DPLL Algorithms
The Goldreich’s function has n binary inputs and n binary outputs. Every output depends on d inputs and is computed from them by the fixed predicate of arity d. Every Goldreich...
Dmitry Itsykson, Dmitry Sokolov
INFOCOM
2005
IEEE
15 years 8 months ago
Bounding the power rate function of wireless ad hoc networks
Abstract— Given a wireless ad hoc network and an endto-end traffic pattern, the power rate function refers to the minimum total power required to support different throughput un...
Yunnan Wu, Qian Zhang, Wenwu Zhu, Sun-Yuan Kung
SBBD
2008
256views Database» more  SBBD 2008»
15 years 4 months ago
Indexing Internal Memory with Minimal Perfect Hash Functions
A perfect hash function (PHF) is an injective function that maps keys from a set S to unique values, which are in turn used to index a hash table. Since no collisions occur, each k...
Fabiano C. Botelho, Hendrickson R. Langbehn, Guilh...
ICML
2008
IEEE
16 years 3 months ago
Apprenticeship learning using linear programming
In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in tha...
Umar Syed, Michael H. Bowling, Robert E. Schapire