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TEC
2008
115views more  TEC 2008»
14 years 11 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 3 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 5 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 1 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 20 days 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