#### Filter Results:

- Full text PDF available (4)

#### Publication Year

2006

2008

- This year (0)
- Last 5 years (0)
- Last 10 years (3)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Julia A. Lasserre, Christopher M. Bishop, Tom Minka
- 2006 IEEE Computer Society Conference on Computer…
- 2006

When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for large-scale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semi-supervised techniques based on generative models in which the majority of the… (More)

For many applications of machine learning the goal is to predict the value of a vector c given the value of a vector x of input features. In a classification problem c represents a discrete class label, whereas in a regression problem it corresponds to one or more continuous variables. From a probabilistic perspective, the goal is to find the conditional… (More)

In machine learning, probabilistic models are described as belonging to one of two categories: generative or discriminative. Generative models are built to understand how samples from a particular category were generated. The category chosen for a new data-point is the category whose model fits the point best. Discriminative models are concerned with… (More)

- Julia A. Lasserre, Anitha Kannan, John M. Winn
- 2007 IEEE Conference on Computer Vision and…
- 2007

A jigsaw is a recently proposed generative model that describes an image as a composition of non-overlapping patches of varying shape, extracted from a latent image. By learning the latent jigsaw image which best explains a set of images, it is possible to discover the shape, size and appearance of repeated structures in the images. A challenge when… (More)

- ‹
- 1
- ›