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» Explaining inferences in Bayesian networks
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153
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SIGMOD
2011
ACM
179views Database» more  SIGMOD 2011»
14 years 6 months ago
No free lunch in data privacy
Differential privacy is a powerful tool for providing privacypreserving noisy query answers over statistical databases. It guarantees that the distribution of noisy query answers...
Daniel Kifer, Ashwin Machanavajjhala
141
Voted
ICIP
2001
IEEE
16 years 5 months ago
Indoor vs outdoor classification of consumer photographs using low-level and semantic features
Scene categorization to indoor vs outdoor may be approached by using low-level features for inferring high-level information about the image. Low-level features such as color and ...
Jiebo Luo, Andreas E. Savakis
116
Voted
COOPIS
2003
IEEE
15 years 8 months ago
Learning to Invoke Web Forms
Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of information agents a...
Nicholas Kushmerick
132
Voted
ECSQARU
1999
Springer
15 years 7 months ago
A Critique of Inductive Causation
: In this paper we consider the problem of inducing causal relations from statistical data. Although it is well known that a correlation does not justify the claim of a causal rela...
Christian Borgelt, Rudolf Kruse
ICMLA
2008
15 years 5 months ago
Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint
The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constrai...
Volker Willert, Julian Eggert, Marc Toussaint, Edg...