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CSDA
2007
134views more  CSDA 2007»
13 years 8 months ago
Variational approximations in Bayesian model selection for finite mixture distributions
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...
Clare A. McGrory, D. M. Titterington
ALT
1998
Springer
14 years 1 months ago
Learning with Refutation
In their pioneering work, Mukouchi and Arikawa modeled a learning situation in which the learner is expected to refute texts which are not representative of L, the class of langua...
Sanjay Jain
CORR
2010
Springer
123views Education» more  CORR 2010»
13 years 7 months ago
Feature Construction for Relational Sequence Learning
Abstract. We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly...
Nicola Di Mauro, Teresa Maria Altomare Basile, Ste...
ICPR
2000
IEEE
14 years 10 months ago
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...
Mário A. T. Figueiredo
ECCC
2008
117views more  ECCC 2008»
13 years 9 months ago
The complexity of learning SUBSEQ(A)
Higman essentially showed that if A is any language then SUBSEQ(A) is regular, where SUBSEQ(A) is the language of all subsequences of strings in A. Let s1, s2, s3, . . . be the sta...
Stephen A. Fenner, William I. Gasarch, Brian Posto...