Add to ORKGInternational audienceGiven any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate. We introduce a family of classifiers that interpolate the two approaches, thus providing a new way to compare them and giving an estimation procedure whose classification performance is well balanced between the bias of generative classifiers and the variance of discriminative ones. We show that an intermediate trade-off between the two strategies is often preferable, both theoretically and in experiments on real data
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program...
We deal with Bayesian generative and discriminative classifiers. Given a model distribution $p(x, y)...
Any binary classifier (or score-function) can be used to define a dissimilarity between two distrib...
International audienceGiven any generative classifier based on an inexact density model, we can defi...
This paper experimentally compares the performance of discriminative and generative classifiers for ...
When labelled training data is plentiful, discriminative techniques are widely used since they give ...
In this work, we investigated the application of score-based gradient learning in discriminative and...
Although discriminatively-trained classifiers are usually more accurate when labeled training data i...
Although discriminatively trained classifiers are usually more accurate when labeled training data ...
We consider the problem of binary classification when the covariates conditioned on the each of the ...
We consider the problem of learning density mixture models for Classification. Traditional learning ...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
AbstractClassical discriminant analysis focusses on Gaussian and nonparametric models where in the s...
Abstract. For classication problems, it is important that the classier is trained with data which is...
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program...
We deal with Bayesian generative and discriminative classifiers. Given a model distribution $p(x, y)...
Any binary classifier (or score-function) can be used to define a dissimilarity between two distrib...
International audienceGiven any generative classifier based on an inexact density model, we can defi...
This paper experimentally compares the performance of discriminative and generative classifiers for ...
When labelled training data is plentiful, discriminative techniques are widely used since they give ...
In this work, we investigated the application of score-based gradient learning in discriminative and...
Although discriminatively-trained classifiers are usually more accurate when labeled training data i...
Although discriminatively trained classifiers are usually more accurate when labeled training data ...
We consider the problem of binary classification when the covariates conditioned on the each of the ...
We consider the problem of learning density mixture models for Classification. Traditional learning ...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
AbstractClassical discriminant analysis focusses on Gaussian and nonparametric models where in the s...
Abstract. For classication problems, it is important that the classier is trained with data which is...
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program...
We deal with Bayesian generative and discriminative classifiers. Given a model distribution $p(x, y)...
Any binary classifier (or score-function) can be used to define a dissimilarity between two distrib...