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Algorithmic Learning in a Random World (Hardcover, 2nd ed. 2022)
Loot Price: R5,346
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Algorithmic Learning in a Random World (Hardcover, 2nd ed. 2022)
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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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