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- Christopher M. Bishop, Nasser M. Nasrabadi
- J. Electronic Imaging
- 2007

Find the secret to improve the quality of life by reading this pattern recognition and machine learning 1st edition. This is a kind of book that you need now. Besides, it can be your favorite book to… (More)

Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes… (More)

- Michael E. Tipping, Christopher M. Bishop
- Neural Computation
- 1999

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing, and visualizing data, although its effectiveness is limited by its global linearity. While… (More)

- Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
- Neural Computation
- 1998

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis,… (More)

- John M. Winn, Christopher M. Bishop
- Journal of Machine Learning Research
- 2005

This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to a Bayesian Network. Like belief propagation, Variational Message Passing… (More)

One of the central issues in the use of principal component analysis (PCA) for data modelling is that of choosing the appropriate number of retained components. This problem was recently addressed… (More)

- Christopher M. Bishop
- Neural Computation
- 1995

It is well known that the addition of noise to the input data of a neural network during training can, in some circumstances, lead to significant improvements in generalization performance. Previous… (More)

Mixture models, in which a probability distribution is represented as a linear superposition of component distributions, are widely used in statistical modelling and pattern recognition. One of the… (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… (More)

- Christopher M. Bishop, Markus Svensén
- UAI
- 2003