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IEEE Transactions on Neural Networks, 2004
In the last few years, the support vector machine (SVM) method has motivated new interest in kernel regression techniques. Although the SVM has been shown to exhibit excellent generalization properties in many experiments, it suffers from several drawbacks, both of a theoretical and a technical nature: the absence of probabilistic outputs, the ...
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In the last few years, the support vector machine (SVM) method has motivated new interest in kernel regression techniques. Although the SVM has been shown to exhibit excellent generalization properties in many experiments, it suffers from several drawbacks, both of a theoretical and a technical nature: the absence of probabilistic outputs, the ...
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Proceedings of the ACM/IEEE international conference on Human-robot interaction, 2007
Good situation awareness (SA) is especially necessary when robots and their operators are not collocated, such as in urban search and rescue (USAR). This paper compares how SA is attained in two systems: one that has an emphasis on video and another that has an emphasis on a three-dimensional map.
Jill L. Drury +2 more
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Good situation awareness (SA) is especially necessary when robots and their operators are not collocated, such as in urban search and rescue (USAR). This paper compares how SA is attained in two systems: one that has an emphasis on video and another that has an emphasis on a three-dimensional map.
Jill L. Drury +2 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, uncorrelated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible.
Si-Bao Chen +3 more
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Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, uncorrelated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible.
Si-Bao Chen +3 more
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Povýběrová Inference: Lasso & Skupinové Lasso
2017The lasso is a popular tool that can be used for variable selection and esti- mation, however, classical statistical inference cannot be applied for its estimates. In this thesis the classical and the group lasso is described together with effici- ent algorithms for the solution.
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