TY  - BOOK
AU  - Bishop, Christopher M.
TI  - Pattern recognition and machine learning
SN  - 9780387310732
U1  - 006.4  
PY  - 2006///
CY  - New York
PB  - Springer
KW  - Machine learning
KW  - Pattern perception
KW  - Pattern recognition systems
KW  - Mathematical statistics
KW  - Artificial intelligence
N2  - Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning
ER  -