Add to ORKGMachine learning practitioners are often faced with a choice between a discrimina-tive and a generative approach to modelling. Here, we present a model based on a hy-brid approach that breaks down some of the barriers between the discriminative and gen-erative points of view, allowing continuous dimensionality reduction of hybrid discrete-continuous data, discriminative classification with missing inputs and manifold learning in-formed by class labels.
This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of...
In this paper, we study semi-supervised learning using hybrid generative/discriminative methods. Spe...
The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabili...
Machine learning practitioners are often faced with a choice between a discriminative and a generati...
Although discriminatively trained classifiers are usually more accurate when labeled training data ...
Although discriminatively-trained classifiers are usually more accurate when labeled training data i...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
Recent work has shown substantial performance improvements of discriminative probabilistic models ov...
Recent work has shown substantial performance improvements of discriminative probabilistic models ov...
In machine learning, probabilistic models are described as be-longing to one of two categories: gene...
When labelled training data is plentiful, discriminative techniques are widely used since they give ...
Supervised learning is difficult with high dimensional input spacesand very small training sets, but...
Learning models for detecting and classifying object categories is a challenging problem in machine ...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of...
In this paper, we study semi-supervised learning using hybrid generative/discriminative methods. Spe...
The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabili...
Machine learning practitioners are often faced with a choice between a discriminative and a generati...
Although discriminatively trained classifiers are usually more accurate when labeled training data ...
Although discriminatively-trained classifiers are usually more accurate when labeled training data i...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
In discriminant analysis, probabilistic generative and discriminative approaches represent two parad...
Recent work has shown substantial performance improvements of discriminative probabilistic models ov...
Recent work has shown substantial performance improvements of discriminative probabilistic models ov...
In machine learning, probabilistic models are described as be-longing to one of two categories: gene...
When labelled training data is plentiful, discriminative techniques are widely used since they give ...
Supervised learning is difficult with high dimensional input spacesand very small training sets, but...
Learning models for detecting and classifying object categories is a challenging problem in machine ...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of...
In this paper, we study semi-supervised learning using hybrid generative/discriminative methods. Spe...
The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabili...