Alogorithmic Learning in a Random World,
V. Vovk, A.Gammerman and G. Shafer, 2005
Conformal predictions, the main topic of this monograph,
is an exciting new method of machine learning. Conformal predictors
are among the most accurate methods of machine learning, and
unlike other sate-of-the-art methods, they provide information
about their own accuracy and reliability.
The monograph includes a rigorous mathematical theory and
careful experimental work. It demonstrates mathematically
the validity of the reliability claimed by conformal predictors
when they are applied to independent and identically distributed
data, and it confirms experimentally that the accuracy is
sufficient for many practical problems. Later chapters of
the monograph generalize these results from independent and
identically distributed data to models called repetitive
structures, which originate in the algorithmic theory
of randomness and statistical physics. The approach is flexible
enough to incorporate most existing methods of machine learning,
including newer methods such as boosting and support vector
machines and older methods such as nearest neighbours and
Topics and features:
- Describes how conformal predictors yield accurate and reliable
predictions complemented with quantatitive measures of their
accuracy and reliability.
- Gives equal attention to classification and regression problems.
- Explains how to apply the new algorithms to real-world data
- Explains connections with Kolmogorov's algorithmic randomness,
recent work in machine learning, and older work in statistics.
- Develops new methods of probability forecasting and shows
how to use them for prediction in causal networks.
- Researchers in computer science, statistics, and artificial
intelligence will find the book an authoritative and rigorous
treatment of some of the most promising new developments in
machine learning. Practitioners and students in all areas
of research that use statistical prediction or machine learning
will find exciting new methods.
Wed, 17-Jun-2009 15:57
Department of Computer Science,
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