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
The goal of pattern classification can be approached from two points of view: informative- where the...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
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 ...
Evaluation metric plays a critical role in achieving the optimal classifier during the classificatio...
Although discriminatively trained classifiers are usually more accurate when labeled training data ...
In this work, we investigated the application of score-based gradient learning in discriminative and...
Abstract. For classication problems, it is important that the classier is trained with data which is...
Although discriminatively-trained classifiers are usually more accurate when labeled training data i...
In statistical pattern classification, generative approaches, such as linear discriminant analysis (...
We consider the problem of learning density mixture models for Classification. Traditional learning ...
Abstract. Discriminative and generative methods provide two distinct approaches to machine learning ...
In statistical pattern classification, generative approaches, such as linear discriminant analysis (...
The goal of pattern classification can be approached from two points of view: informative- where the...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
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 ...
Evaluation metric plays a critical role in achieving the optimal classifier during the classificatio...
Although discriminatively trained classifiers are usually more accurate when labeled training data ...
In this work, we investigated the application of score-based gradient learning in discriminative and...
Abstract. For classication problems, it is important that the classier is trained with data which is...
Although discriminatively-trained classifiers are usually more accurate when labeled training data i...
In statistical pattern classification, generative approaches, such as linear discriminant analysis (...
We consider the problem of learning density mixture models for Classification. Traditional learning ...
Abstract. Discriminative and generative methods provide two distinct approaches to machine learning ...
In statistical pattern classification, generative approaches, such as linear discriminant analysis (...
The goal of pattern classification can be approached from two points of view: informative- where the...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...