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» Cryptographic Assumptions: A Position Paper
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ECML
2005
Springer
15 years 3 months ago
Learning from Positive and Unlabeled Examples with Different Data Distributions
Abstract. We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive exam...
Xiaoli Li, Bing Liu
ICALP
2010
Springer
15 years 2 months ago
The Positive Semidefinite Grothendieck Problem with Rank Constraint
Given a positive integer n and a positive semidefinite matrix A = (Aij ) ∈ Rm×m the positive semidefinite Grothendieck problem with rank-nconstraint is (SDPn) maximize mX i=1 ...
Jop Briët, Fernando Mário de Oliveira ...
MVA
2007
110views Computer Vision» more  MVA 2007»
14 years 11 months ago
Propagation of Uncertainty in Landmark Based Self-localization of Autonomous Mobile Robots
This paper presents uncertainty propagation in landmark based position estimation methods. Analysis of two methods has been carried out where robot position is estimated by detect...
Abdul Bais, Robert Sablatnig, Yahya M. Khawaja, Gr...
ACNS
2011
Springer
237views Cryptology» more  ACNS 2011»
14 years 1 months ago
Private Discovery of Common Social Contacts
The increasing use of computing devices for social interactions propels the proliferation of online social applications, yet, it prompts a number of privacy concerns. One common p...
Emiliano De Cristofaro, Mark Manulis, Bertram Poet...
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
15 years 10 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto