Algorithmic Learning In A Random World
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Author |
: Vladimir Vovk |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 332 |
Release |
: 2005-12-05 |
ISBN-10 |
: 9780387250618 |
ISBN-13 |
: 0387250611 |
Rating |
: 4/5 (18 Downloads) |
Synopsis Algorithmic Learning in a Random World by : Vladimir Vovk
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Author |
: Vladimir Vovk |
Publisher |
: Springer Nature |
Total Pages |
: 490 |
Release |
: 2022-12-13 |
ISBN-10 |
: 9783031066498 |
ISBN-13 |
: 3031066499 |
Rating |
: 4/5 (98 Downloads) |
Synopsis Algorithmic Learning in a Random World by : Vladimir Vovk
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.
Author |
: Vladimir Vovk |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 344 |
Release |
: 2005-03-22 |
ISBN-10 |
: 0387001522 |
ISBN-13 |
: 9780387001524 |
Rating |
: 4/5 (22 Downloads) |
Synopsis Algorithmic Learning in a Random World by : Vladimir Vovk
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Author |
: Vladimir Vovk |
Publisher |
: |
Total Pages |
: 324 |
Release |
: 2005 |
ISBN-10 |
: 9780387250 |
ISBN-13 |
: 9789780387259 |
Rating |
: 4/5 (50 Downloads) |
Synopsis Algorithmic Learning in a Random World by : Vladimir Vovk
Author |
: Vineeth Balasubramanian |
Publisher |
: Newnes |
Total Pages |
: 323 |
Release |
: 2014-04-23 |
ISBN-10 |
: 9780124017153 |
ISBN-13 |
: 0124017150 |
Rating |
: 4/5 (53 Downloads) |
Synopsis Conformal Prediction for Reliable Machine Learning by : Vineeth Balasubramanian
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Author |
: Shai Shalev-Shwartz |
Publisher |
: Cambridge University Press |
Total Pages |
: 415 |
Release |
: 2014-05-19 |
ISBN-10 |
: 9781107057135 |
ISBN-13 |
: 1107057132 |
Rating |
: 4/5 (35 Downloads) |
Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author |
: Pedro Domingos |
Publisher |
: Basic Books |
Total Pages |
: 354 |
Release |
: 2015-09-22 |
ISBN-10 |
: 9780465061921 |
ISBN-13 |
: 0465061923 |
Rating |
: 4/5 (21 Downloads) |
Synopsis The Master Algorithm by : Pedro Domingos
Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
Author |
: Stephen Marsland |
Publisher |
: CRC Press |
Total Pages |
: 407 |
Release |
: 2011-03-23 |
ISBN-10 |
: 9781420067194 |
ISBN-13 |
: 1420067192 |
Rating |
: 4/5 (94 Downloads) |
Synopsis Machine Learning by : Stephen Marsland
Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but
Author |
: Florian Jaton |
Publisher |
: MIT Press |
Total Pages |
: 401 |
Release |
: 2021-04-27 |
ISBN-10 |
: 9780262542142 |
ISBN-13 |
: 0262542145 |
Rating |
: 4/5 (42 Downloads) |
Synopsis The Constitution of Algorithms by : Florian Jaton
A laboratory study that investigates how algorithms come into existence. Algorithms--often associated with the terms big data, machine learning, or artificial intelligence--underlie the technologies we use every day, and disputes over the consequences, actual or potential, of new algorithms arise regularly. In this book, Florian Jaton offers a new way to study computerized methods, providing an account of where algorithms come from and how they are constituted, investigating the practical activities by which algorithms are progressively assembled rather than what they may suggest or require once they are assembled.
Author |
: Ankur Moitra |
Publisher |
: Cambridge University Press |
Total Pages |
: 161 |
Release |
: 2018-09-27 |
ISBN-10 |
: 9781107184589 |
ISBN-13 |
: 1107184584 |
Rating |
: 4/5 (89 Downloads) |
Synopsis Algorithmic Aspects of Machine Learning by : Ankur Moitra
Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.