Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
From the reviews:
"Algorithmic Learning in a Random World has ten chapters, three appendices, and extensive references. Each chapter ends with a section containing comments, historical discussion, and bibliographical remarks. ... The material is developed well and reasonably easy to follow ... . the text is very readable. ... is doubtless an important reference summarizing a large body of work by the authors and their graduate students. Academics involved with new implementations and empirical studies of machine learning techniques may find it useful too." (James Law, SIGACT News, Vol. 37 (4), 2006)
Conformal prediction is a valuable new method of machine learning. Conformal predictors are among the most accurate methods of machine learning, and unlike other state-of-the-art methods, they provide information about their own accuracy and reliability.
This new monograph integrates mathematical theory and revealing 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 generalize these results 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 neighbors and the bootstrap.
Topics and Features:
* Describes how conformal predictors yield accurate and reliable predictions, complemented with quantitative measures of their accuracy and reliability
* Handles both classification and regression problems
* Explains how to apply the new algorithms to real-world data sets
* Demonstrates the infeasibility of some standard prediction tasks
* 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 quantitative prediction or machine learning will learn about important new methods.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Spese di spedizione:
EUR 3,75
In U.S.A.
Descrizione libro Hardcover. Condizione: new. New. Fast Shipping and good customer service. Codice articolo Holz_New_0387001522
Descrizione libro Hardcover. Condizione: new. New. Codice articolo Wizard0387001522
Descrizione libro Condizione: New. New. In shrink wrap. Looks like an interesting title! 1.52. Codice articolo Q-0387001522
Descrizione libro Hardcover. Condizione: new. New Copy. Customer Service Guaranteed. Codice articolo think0387001522
Descrizione libro Condizione: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Codice articolo ria9780387001524_lsuk
Descrizione libro Condizione: New. Codice articolo 1694268-n
Descrizione libro Hardcover. Condizione: new. Codice articolo 9780387001524
Descrizione libro Condizione: New. Buy with confidence! Book is in new, never-used condition. Codice articolo bk0387001522xvz189zvxnew
Descrizione libro Condizione: New. New! This book is in the same immaculate condition as when it was published. Codice articolo 353-0387001522-new
Descrizione libro Condizione: New. Codice articolo 1694268-n