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GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
14 years 3 months ago
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Surrogate-Assisted Memetic Algorithm(SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since mos...
Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendh...
ICPR
2008
IEEE
14 years 11 months ago
Component-wise parameter smoothing for learning mixture models
In this paper, we propose a novel component-wise smoothing algorithm that constructs a hierarchy (or family) of smoothened log-likelihood surfaces. Our approach first smoothens th...
Bala Rajaratnam, Chandan K. Reddy
ACL
2004
13 years 11 months ago
Annealing Techniques For Unsupervised Statistical Language Learning
Exploiting unannotated natural language data is hard largely because unsupervised parameter estimation is hard. We describe deterministic annealing (Rose et al., 1990) as an appea...
Noah A. Smith, Jason Eisner
GECCO
2008
Springer
123views Optimization» more  GECCO 2008»
13 years 10 months ago
Hierarchical evolution of linear regressors
We propose an algorithm for function approximation that evolves a set of hierarchical piece-wise linear regressors. The algorithm, named HIRE-Lin, follows the iterative rule learn...
Francesc Teixidó-Navarro, Albert Orriols-Pu...
ICML
2010
IEEE
13 years 10 months ago
Hilbert Space Embeddings of Hidden Markov Models
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
Le Song, Sajid M. Siddiqi, Geoffrey J. Gordon, Ale...