Add to ORKGSome learning techniques for classification tasks work indirectly, by first trying to fit a full probabilistic model to the observed data. Whether this is a good idea or not depends on the robustness with respect to deviations from the postulated model. We study this question experimentally in a restricted, yet non-trivial and interesting case: we consider a conditionally independent attribute (CIA) model which postulates a single binary-valued hidden variable z on which all other attributes (i.e., the target and the observables) depend. In this model, finding the most likely value of any one variable (given known values for the others) reduces to testing a linear function of the observed values. We learn CIA with two techniques: the standa...
In this dissertation, we explore two fundamental sets of inference problems arising in machine learn...
In data mining we often have to learn from biased data, because, for instance, data comes from diffe...
We describe and analyze efficient algorithms for learning a linear predictor from examples when the ...
This paper addresses the problem of classification in situations where the data distribution is not ...
International audiencePartially supervised learning extends both supervised and unsu-pervised learni...
We provide some simple theoretical results that justify incorporating machinelearning in a standard ...
A serious problem in learning probabilistic models is the presence of hidden variables. These variab...
We study the problem of learning Bayesian classifiers (BC)when the true class label of the training ...
A novel approach to semi-supervised learning for classical Fisher linear discriminant analysis is pr...
The conditional independence assumption of naive Bayes essentially ignores attribute dependencies an...
Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been sh...
Classical learning theory is based on a tight linkage between hypothesis space (a class of function ...
Supervised methods of classification naturally exploit linear and non linear relationships between e...
Due to spurious correlations, machine learning systems often fail to generalize to environments whos...
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the assoc...
In this dissertation, we explore two fundamental sets of inference problems arising in machine learn...
In data mining we often have to learn from biased data, because, for instance, data comes from diffe...
We describe and analyze efficient algorithms for learning a linear predictor from examples when the ...
This paper addresses the problem of classification in situations where the data distribution is not ...
International audiencePartially supervised learning extends both supervised and unsu-pervised learni...
We provide some simple theoretical results that justify incorporating machinelearning in a standard ...
A serious problem in learning probabilistic models is the presence of hidden variables. These variab...
We study the problem of learning Bayesian classifiers (BC)when the true class label of the training ...
A novel approach to semi-supervised learning for classical Fisher linear discriminant analysis is pr...
The conditional independence assumption of naive Bayes essentially ignores attribute dependencies an...
Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been sh...
Classical learning theory is based on a tight linkage between hypothesis space (a class of function ...
Supervised methods of classification naturally exploit linear and non linear relationships between e...
Due to spurious correlations, machine learning systems often fail to generalize to environments whos...
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the assoc...
In this dissertation, we explore two fundamental sets of inference problems arising in machine learn...
In data mining we often have to learn from biased data, because, for instance, data comes from diffe...
We describe and analyze efficient algorithms for learning a linear predictor from examples when the ...