Adapting Machine Learning To Non Stationary Environments
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Machine Learning In Non Stationary Environments
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Author : Masashi Sugiyama
language : en
Publisher: MIT Press
Release Date : 2012-03-30
Machine Learning In Non Stationary Environments written by Masashi Sugiyama and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-30 with Computers categories.
Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
Learning In Non Stationary Environments
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Author : Moamar Sayed-Mouchaweh
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-04-13
Learning In Non Stationary Environments written by Moamar Sayed-Mouchaweh 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 2012-04-13 with Technology & Engineering categories.
Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Adapting Machine Learning To Non Stationary Environments
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Author : Wintheiser Donnie
language : en
Publisher:
Release Date : 2023-04-04
Adapting Machine Learning To Non Stationary Environments written by Wintheiser Donnie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-04 with Computers categories.
Machine learning stimulates a broad range of computational methods that exploit experience, which typically takes the form of electronic data, to make profitable decisions or accurate predictions. To date, the machine learning models have been applied to extensive application domains across diverse fields, including but not limited to computer vision [1, 2, 3], natural language processing [4, 5, 6], robotic control [7, 8], and cyber security [9, 10, 11].
Machine Learning In Non Stationary Environments
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Author : Masashi Sugiyama
language : en
Publisher: MIT Press
Release Date : 2012
Machine Learning In Non Stationary Environments written by Masashi Sugiyama and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
Dealing with non-stationarity is one of modem machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity.
Artificial Neural Networks And Machine Learning Icann 2012
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Author : Alessandro Villa
language : en
Publisher: Springer
Release Date : 2012-09-19
Artificial Neural Networks And Machine Learning Icann 2012 written by Alessandro Villa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-19 with Computers categories.
The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.
Adaptive And Intelligent Systems
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Author : Abdelhamid Bouchachia
language : en
Publisher: Springer
Release Date : 2014-08-13
Adaptive And Intelligent Systems written by Abdelhamid Bouchachia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-13 with Computers categories.
This book constitutes the proceedings of the International Conference on Adaptive and Intelligent Systems, ICAIS 2014, held in Bournemouth, UK, in September 2014. The 19 full papers included in these proceedings together with the abstracts of 4 invited talks, were carefully reviewed and selected from 32 submissions. The contributions are organized under the following topical sections: advances in feature selection; clustering and classification; adaptive optimization; advances in time series analysis.
Machine Learning For Adaptive Many Core Machines A Practical Approach
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Author : Noel Lopes
language : en
Publisher: Springer
Release Date : 2014-06-28
Machine Learning For Adaptive Many Core Machines A Practical Approach written by Noel Lopes and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Technology & Engineering categories.
The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.
Statistical Machine Learning
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Author : Richard Golden
language : en
Publisher: CRC Press
Release Date : 2020-06-24
Statistical Machine Learning written by Richard Golden and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-24 with Computers categories.
The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.
Multiple Classifier Systems
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Author : Jón Atli Benediktsson
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-06-02
Multiple Classifier Systems written by Jón Atli Benediktsson 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 2009-06-02 with Computers categories.
This book constitutes the refereed proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, held in Reykjavik, Iceland, in June 2009. The 52 revised full papers presented together with 2 invited papers were carefully reviewed and selected from more than 70 initial submissions. The papers are organized in topical sections on ECOC boosting and bagging, MCS in remote sensing, unbalanced data and decision templates, stacked generalization and active learning, concept drift, missing values and random forest, SVM ensembles, fusion of graphics, concepts and categorical data, clustering, and finally theory, methods and applications of MCS.
Multiple Classifier Systems
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Author : Michal Haindl
language : en
Publisher: Springer
Release Date : 2007-06-21
Multiple Classifier Systems written by Michal Haindl and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-21 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. It covers kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.