ACL
2006
13 years 7 months ago
2006
Certain distinctions made in the lexicon of one language may be redundant when translating into another language. We quantify redundancy among source types by the similarity of th...
NIPS
2007
13 years 7 months ago
2007
Bayesian model averaging, model selection and their approximations such as BIC are generally statistically consistent, but sometimes achieve slower rates of convergence than other...
SDM
2008
SIAM
13 years 7 months ago
2008
SIAM
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
IJCAI
2007
13 years 7 months ago
2007
We present MBoost, a novel extension to AdaBoost that extends boosting to use multiple weak learners explicitly, and provides robustness to learning models that overfit or are po...
GEOINFO
2007
13 years 7 months ago
2007
Abstract. We present a method to perform model selection based on predictive density in a class of spatio-temporal dynamic generalized linear models for areal data. These models as...
SDM
2010
SIAM
13 years 7 months ago
2010
SIAM
We give a permutation approach to validation (estimation of out-sample error). One typical use of validation is model selection. We establish the legitimacy of the proposed permut...
ICML
1994
IEEE
13 years 9 months ago
1994
IEEE
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected...
ANNPR
2006
Springer
13 years 10 months ago
2006
Springer
Abstract. In our previous work we have shown that Mahalanobis kernels are useful for support vector classifiers both from generalization ability and model selection speed. In this ...
HAIS
2010
Springer
13 years 11 months ago
2010
Springer
The intuition behind ensembles is that different prediciton models compensate each other’s errors if one combines them in an appropriate way. In case of large ensembles a lot of...
GECCO
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. We discuss the problem of model selection in Genetic Programming using the framework provided by Statistical Learning Theory, i.e. Vapnik-Chervonenkis theory (VC). We pre...