£124.07

Algorithmic Learning in a Random World

AI & Machine Learning

Price data last checked 135 day(s) ago - refreshing...

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

This is the usual price. Wait for it to drop, or tell us your number.

£124 today · usual range £0–£0 · best ever £99

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 596 days • 596 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 596 days • 5 price ranges

Days at Price

Current Price

Price Analysis

Most common range: £114-122 (275 days, 46.1%)

Price range: £99 - £138

Price levels: 5 price ranges over 596 days

Description

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.

Product Specifications

Format: paperback
ASIN: 3031066510
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 14 December 2023
Listed Since: 18 November 2023

Barcode

No barcode data available

Algorithmic Learning in a Random World

This is the usual price. Wait for it to drop, or tell us your number.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Algorithmic Learning in a Random World

Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

Theory of Stochastic Objects: Probability, Stochastic Processes and Inference (Chapman & Hall/CRC Texts in Statistical Science)

Machine Learning – A Probabilistic Perspective

High Dimensional Probability VIII: The Oaxaca Volume: 74 (Progress in Probability, 74)

Data Science and Machine Learning: Mathematical and Statistical Methods, Second Edition (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Computational Statistics (Statistics and Computing)

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

Computational Statistics (Statistics and Computing)

Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling

Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

Synergies of Soft Computing and Statistics for Intelligent Data Analysis: 190 (Advances in Intelligent Systems and Computing, 190)

Introduction to Probabilistic and Statistical Methods with Examples in R: 176 (Intelligent Systems Reference Library, 176)

The Oxford Handbook of Functional Data Analysis

Coding Ockham's Razor

The Design and Analysis of Computer Experiments (Springer Series in Statistics)

A User's Guide to Business Analytics

Nonparametric Methods in Change Point Problems: 243 (Mathematics and Its Applications, 243)

Conditional Measures and Applications: 271 (Monographs and Textbooks in Pure and Applied Mathematics)

Statistical Theory: A Concise Introduction (Chapman & Hall/CRC Texts in Statistical Science)

A First Course in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

An Introduction to Statistical Analysis of Random Arrays

Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition

Predictive Statistics: Analysis and Inference beyond Models: 46 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46)