#### Filter Results:

- Full text PDF available (105)

#### Publication Year

1991

2018

- This year (13)
- Last 5 years (41)
- Last 10 years (74)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Cell Type

#### Data Set Used

#### Method

#### Organism

Learn More

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

his book provides an introduction to the eld of pattern recognition and machine earning. It gives an overview of several asic and advanced topics in machine earning theory. The book is definitelyâ€¦ (More)

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions ofâ€¦ (More)

- Christopher M. Bishop, Markus SvensÃ©n, 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,â€¦ (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 visualising data, although its effectiveness is limited by its global linearity. Whileâ€¦ (More)

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

Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniquesâ€¦ (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 addressedâ€¦ (More)

- Christopher M. Bishop
- Information science and statistics
- 2007

We may not be able to make you love reading, but pattern recognition and machine learning 1st edition will lead you to love reading starting from now. Book is the window to open the new world. Theâ€¦ (More)

The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions in terms of aâ€¦ (More)

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. Previousâ€¦ (More)

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