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CIKM
2005
Springer
14 years 2 months ago
Information retrieval and machine learning for probabilistic schema matching
Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distribute...
Henrik Nottelmann, Umberto Straccia
CISST
2004
164views Hardware» more  CISST 2004»
13 years 10 months ago
Probabilistic Region Relevance Learning for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
Iker Gondra, Douglas R. Heisterkamp
STOC
2007
ACM
132views Algorithms» more  STOC 2007»
14 years 9 months ago
On the convergence of Newton's method for monotone systems of polynomial equations
Monotone systems of polynomial equations (MSPEs) are systems of fixed-point equations X1 = f1(X1, . . . , Xn), . . . , Xn = fn(X1, . . . , Xn) where each fi is a polynomial with p...
Stefan Kiefer, Michael Luttenberger, Javier Esparz...
COLT
2006
Springer
14 years 13 days ago
Learning Rational Stochastic Languages
Given a finite set of words w1, . . . , wn independently drawn according to a fixed unknown distribution law P called a stochastic language, an usual goal in Grammatical Inference ...
François Denis, Yann Esposito, Amaury Habra...
CORR
2008
Springer
143views Education» more  CORR 2008»
13 years 8 months ago
Convergence Thresholds of Newton's Method for Monotone Polynomial Equations
Abstract. Monotone systems of polynomial equations (MSPEs) are systems of fixedpoint equations X1 = f1(X1, . . . , Xn), . . . , Xn = fn(X1, . . . , Xn) where each fi is a polynomia...
Javier Esparza, Stefan Kiefer, Michael Luttenberge...