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» Lattice-based computation of Boolean functions
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FOCS
1990
IEEE
13 years 11 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum
NIPS
2001
13 years 9 months ago
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
Roni Khardon, Dan Roth, Rocco A. Servedio
DAC
2009
ACM
14 years 8 months ago
Nanoscale digital computation through percolation
In this study, we apply a novel synthesis technique for implementing robust digital computation in nanoscale lattices with random interconnects: percolation theory on random graph...
Mustafa Altun, Marc D. Riedel, Claudia Neuhauser
CPC
1998
91views more  CPC 1998»
13 years 7 months ago
Complexity and Probability of Some Boolean Formulas
For any Boolean functionf letL(f) be its formulasizecomplexityin the basis f^ 1g. For every n and every k n=2, we describe a probabilistic distribution on formulas in the basis f^...
Petr Savický
TVLSI
1998
124views more  TVLSI 1998»
13 years 7 months ago
Computing support-minimal subfunctions during functional decomposition
Abstract— The growing popularity of look-up table (LUT)based field programmable gate arrays (FPGA’s) has renewed the interest in functional or Roth–Karp decomposition techni...
Christian Legl, Bernd Wurth, Klaus Eckl