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JMLR
2008
230views more  JMLR 2008»
15 years 6 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
BSL
2004
110views more  BSL 2004»
15 years 5 months ago
Notes on quasiminimality and excellence
This paper ties together much of the model theory of the last 50 years. Shelah's attempts to generalize the Morley theorem beyond first order logic led to the notion of excel...
John T. Baldwin
MP
2002
176views more  MP 2002»
15 years 5 months ago
UOBYQA: unconstrained optimization by quadratic approximation
UOBYQA is a new algorithm for general unconstrained optimization calculations, that takes account of the curvature of the objective function, F say, by forming quadratic models by ...
M. J. D. Powell
BMCBI
2011
15 years 16 days ago
Analysis on the reconstruction accuracy of the Fitch method for inferring ancestral states
Background: As one of the most widely used parsimony methods for ancestral reconstruction, the Fitch method minimizes the total number of hypothetical substitutions along all bran...
Jialiang Yang, Jun Li, Liuhuan Dong, Stefan Gr&uum...
CEC
2011
IEEE
14 years 6 months ago
Stochastic Natural Gradient Descent by estimation of empirical covariances
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
Luigi Malagò, Matteo Matteucci, Giovanni Pi...