Machine Learning Ecml 2002

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Machine Learning Ecml 2002

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Author : Tapio Elomaa
language : en
Publisher: Springer
Release Date : 2002-01-01

Machine Learning Ecml 2002 written by Tapio Elomaa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-01-01 with Computers categories.

This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002. The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.

Machine Learning Ecml 2002

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Author : Tapio Elomaa
language : en
Publisher: Springer
Release Date : 2002-08-05

Machine Learning Ecml 2002 written by Tapio Elomaa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-08-05 with Computers categories.

This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002. The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.

Machine Learning

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Author : Tapio Elomaa
language : en
Publisher:
Release Date : 2014-01-15

Machine Learning written by Tapio Elomaa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.

Machine Learning Ecml

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Author :
language : en
Publisher:
Release Date : 2003

Machine Learning Ecml written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Induction (Logic) categories.

Conformal Prediction For Reliable Machine Learning

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Author : Vineeth Balasubramanian
language : en
Publisher: Newnes
Release Date : 2014-04-23

Conformal Prediction For Reliable Machine Learning written by Vineeth Balasubramanian and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-23 with Computers categories.

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

Machine Learning And Data Mining In Pattern Recognition

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Author : Petra Perner
language : en
Publisher: Springer
Release Date : 2007-08-28

Machine Learning And Data Mining In Pattern Recognition written by Petra Perner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-28 with Computers categories.

Ever wondered what the state of the art is in machine learning and data mining? Well, now you can find out. This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, held in Leipzig, Germany, in July 2007. The 66 revised full papers presented together with 1 invited talk were carefully reviewed and selected from more than 250 submissions. The papers are organized in topical sections.

Application Of Machine Learning

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Author : Yagang Zhang
language : en
Publisher: BoD – Books on Demand
Release Date : 2010-02-01

Application Of Machine Learning written by Yagang Zhang and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-01 with Computers categories.

The goal of this book is to present the latest applications of machine learning, which mainly include: speech recognition, traffic and fault classification, surface quality prediction in laser machining, network security and bioinformatics, enterprise credit risk evaluation, and so on. This book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The wide scope of the book provides them with a good introduction to many application researches of machine learning, and it is also the source of useful bibliographical information.

Machine Learning And Knowledge Discovery For Engineering Systems Health Management

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Author : Ashok N. Srivastava
language : en
Publisher: CRC Press
Release Date : 2016-04-19

Machine Learning And Knowledge Discovery For Engineering Systems Health Management written by Ashok N. Srivastava and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Computers categories.

This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.

Ensemble Machine Learning

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Author : Cha Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-17

Ensemble Machine Learning written by Cha Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-17 with Computers categories.

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

Machine Learning And Data Mining

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Author : Igor Kononenko
language : en
Publisher: Elsevier
Release Date : 2007-04-30

Machine Learning And Data Mining written by Igor Kononenko and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-30 with Computers categories.

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions. - Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining - A valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions