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This book brings together historical notes, reviews of research
developments, fresh ideas on how to make VC (Vapnik-Chervonenkis)
guarantees tighter, and new technical contributions in the areas of
machine learning, statistical inference, classification,
algorithmic statistics, and pattern recognition. The contributors
are leading scientists in domains such as statistics, mathematics,
and theoretical computer science, and the book will be of interest
to researchers and graduate students in these domains.
This book brings together historical notes, reviews of research
developments, fresh ideas on how to make VC (Vapnik-Chervonenkis)
guarantees tighter, and new technical contributions in the areas of
machine learning, statistical inference, classification,
algorithmic statistics, and pattern recognition. The contributors
are leading scientists in domains such as statistics, mathematics,
and theoretical computer science, and the book will be of interest
to researchers and graduate students in these domains.
This book is about conformal prediction, an approach to prediction
that originated in machine learning in the late 1990s. The main
feature of conformal prediction is the principled treatment of the
reliability of predictions. The prediction algorithms described -
conformal predictors - are provably valid in the sense that they
evaluate the reliability of their own predictions in a way that is
neither over-pessimistic nor over-optimistic (the latter being
especially dangerous). The approach is still flexible enough to
incorporate most of the existing powerful methods of machine
learning. The book covers both key conformal predictors and the
mathematical analysis of their properties. Algorithmic Learning in
a Random World contains, in addition to proofs of validity, results
about the efficiency of conformal predictors. The only assumption
required for validity is that of "randomness" (the prediction
algorithm is presented with independent and identically distributed
examples); in later chapters, even the assumption of randomness is
significantly relaxed. Interesting results about efficiency are
established both under randomness and under stronger assumptions.
Since publication of the First Edition in 2005 conformal prediction
has found numerous applications in medicine and industry, and is
becoming a popular machine-learning technique. This Second Edition
contains three new chapters. One is about conformal predictive
distributions, which are more informative than the set predictions
produced by standard conformal predictors. Another is about the
efficiency of ways of testing the assumption of randomness based on
conformal prediction. The third new chapter harnesses conformal
testing procedures for protecting machine-learning algorithms
against changes in the distribution of the data. In addition, the
existing chapters have been revised, updated, and expanded.
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Conformal and Probabilistic Prediction with Applications - 5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016, Proceedings (Paperback, 1st ed. 2016)
Alexander Gammerman, Zhiyuan Luo, Jesus Vega, Vladimir Vovk
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R2,280
Discovery Miles 22 800
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 5th
International Symposium on Conformal and Probabilistic Prediction
with Applications, COPA 2016, held in Madrid, Spain, in April 2016.
The 14 revised full papers presented together with 1 invited paper
were carefully reviewed and selected from 23 submissions and cover
topics on theory of conformal prediction; applications of conformal
prediction; and machine learning.
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Statistical Learning and Data Sciences - Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings (Paperback, 2015 ed.)
Alexander Gammerman, Vladimir Vovk, Harris Papadopoulos
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R2,949
Discovery Miles 29 490
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the Third
International Symposium on Statistical Learning and Data Sciences,
SLDS 2015, held in Egham, Surrey, UK, April 2015. The 36 revised
full papers presented together with 2 invited papers were carefully
reviewed and selected from 59 submissions. The papers are organized
in topical sections on statistical learning and its applications,
conformal prediction and its applications, new frontiers in data
analysis for nuclear fusion, and geometric data analysis.
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