Open AccessComputer ScienceMathematics

Pattern Recognition and Machine Learning

Radford M. Neal

2006.8.1TECHNOMETRICS

DOI: 10.1117/1.2819119

tlooto Summary

Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.

Abstract

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Citation format

NEAL, Radford M. Pattern recognition and machine learning. TECHNOMETRICS, 2006, 16: 049901.