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ML
2008
ACM
135views Machine Learning» more  ML 2008»
14 years 9 months ago
Compiling pattern matching to good decision trees
We address the issue of compiling ML pattern matching to compact and efficient decisions trees. Traditionally, compilation to decision trees is optimized by (1) implementing decis...
Luc Maranget
69
Voted
ML
2008
ACM
115views Machine Learning» more  ML 2008»
14 years 9 months ago
Calling variadic functions from a strongly-typed language
The importance of providing a mechanism to call C functions from high-level languages has been understood for many years and, these days, almost all statically-typed high-level-la...
Matthias Blume, Mike Rainey, John H. Reppy
TIT
2008
102views more  TIT 2008»
14 years 9 months ago
On Low-Complexity Maximum-Likelihood Decoding of Convolutional Codes
Abstract--This letter considers the average complexity of maximum-likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path take...
Jie Luo
TCOM
2008
82views more  TCOM 2008»
14 years 9 months ago
Detecting the number of signals in wireless DS-CDMA networks
In this paper, a new information theoretic algorithm is proposed for signal enumeration in DS-CDMA networks. The approach is based on the predictive description length (PDL) of the...
Shahrokh Valaee, Shahram Shahbazpanahi
62
Voted
TSP
2008
117views more  TSP 2008»
14 years 9 months ago
Asymptotically Efficient Reduced Complexity Frequency Offset and Channel Estimators for Uplink MIMO-OFDMA Systems
In this paper, we address the joint data-aided estimation of frequency offsets and channel coefficients in uplink MIMO-OFDMA systems. As the Maximum Likelihood (ML) estimator is i...
Serdar Sezginer, Pascal Bianchi