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» Lattice-based computation of Boolean functions
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FOCS
1990
IEEE
15 years 3 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
15 years 1 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
16 years 22 days 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»
14 years 11 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»
14 years 11 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