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» On Learning Boolean Functions
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ICCAD
1999
IEEE
148views Hardware» more  ICCAD 1999»
15 years 11 months ago
SAT based ATPG using fast justification and propagation in the implication graph
In this paper we present new methods for fast justification and propagation in the implication graph (IG) which is the core data structure of our SAT based implication engine. As ...
Paul Tafertshofer, Andreas Ganz
DAC
1994
ACM
15 years 10 months ago
Exact Minimum Cycle Times for Finite State Machines
In current research, the minimum cycle times of finite state machines are estimated by computing the delays of the combinational logic in the finite state machines. Even though th...
William K. C. Lam, Robert K. Brayton, Alberto L. S...
VDA
2010
206views Visualization» more  VDA 2010»
15 years 9 months ago
Visual discovery in multivariate binary data
This paper presents the concept of Monotone Boolean Function Visual Analytics (MBFVA) and its application to the medical domain. The medical application is concerned with discover...
Boris Kovalerchuk, Florian Delizy, Logan Riggs, Ev...
ALT
2000
Springer
16 years 3 months ago
On the Noise Model of Support Vector Machines Regression
Abstract. Support Vector Machines Regression (SVMR) is a learning technique where the goodness of fit is measured not by the usual quadratic loss function (the mean square error),...
Massimiliano Pontil, Sayan Mukherjee, Federico Gir...
UAI
2000
15 years 8 months ago
Utilities as Random Variables: Density Estimation and Structure Discovery
Decision theory does not traditionally include uncertainty over utility functions. We argue that the a person's utility value for a given outcome can be treated as we treat o...
Urszula Chajewska, Daphne Koller