Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning
Author: Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Publsiher: Newnes
Total Pages: 334
Release: 2014-04-23
ISBN 10: 0124017150
ISBN 13: 9780124017153
Language: EN, FR, DE, ES & NL

Conformal Prediction for Reliable Machine Learning Book Review:

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

Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning
Author: Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Publsiher: Morgan Kaufmann
Total Pages: 298
Release: 2014
ISBN 10: 9780123985378
ISBN 13: 0123985374
Language: EN, FR, DE, ES & NL

Conformal Prediction for Reliable Machine Learning Book Review:

"Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of con dence in the predicted labels of new, unclassi ed examples. Con dence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"--

Algorithmic Learning in a Random World

Algorithmic Learning in a Random World
Author: Vladimir Vovk,Alexander Gammerman,Glenn Shafer
Publsiher: Springer Science & Business Media
Total Pages: 324
Release: 2005-03-22
ISBN 10: 9780387001524
ISBN 13: 0387001522
Language: EN, FR, DE, ES & NL

Algorithmic Learning in a Random World Book Review:

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.

Information Processing and Management of Uncertainty in Knowledge Based Systems

Information Processing and Management of Uncertainty in Knowledge Based Systems
Author: Marie-Jeanne Lesot,Susana Vieira,Marek Z. Reformat,João Paulo Carvalho,Anna Wilbik,Bernadette Bouchon-Meunier,Ronald R. Yager
Publsiher: Springer Nature
Total Pages: 833
Release: 2020-06-05
ISBN 10: 3030501531
ISBN 13: 9783030501532
Language: EN, FR, DE, ES & NL

Information Processing and Management of Uncertainty in Knowledge Based Systems Book Review:

This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.

Advances and Trends in Artificial Intelligence From Theory to Practice

Advances and Trends in Artificial Intelligence  From Theory to Practice
Author: Franz Wotawa,Gerhard Friedrich,Ingo Pill,Roxane Koitz-Hristov,Moonis Ali
Publsiher: Springer
Total Pages: 865
Release: 2019-06-28
ISBN 10: 3030229998
ISBN 13: 9783030229993
Language: EN, FR, DE, ES & NL

Advances and Trends in Artificial Intelligence From Theory to Practice Book Review:

This book constitutes the thoroughly refereed proceedings of the 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, held in Graz, Austria, in July 2019. The 41 full papers and 32 short papers presented were carefully reviewed and selected from 151 submissions. The IEA/AIE 2019 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include engineering, science, industry, automation and robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions. IEA/AIE 2019 will have a special focus on automated driving and autonomous systems and also contributions dealing with such systems or their verification and validation as well.

Machine Learning in Non Stationary Environments

Machine Learning in Non Stationary Environments
Author: Masashi Sugiyama,Motoaki Kawanabe
Publsiher: MIT Press
Total Pages: 280
Release: 2012-03-30
ISBN 10: 0262300435
ISBN 13: 9780262300438
Language: EN, FR, DE, ES & NL

Machine Learning in Non Stationary Environments Book Review:

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.

The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory
Author: Vladimir N. Vapnik
Publsiher: Springer Science & Business Media
Total Pages: 188
Release: 2013-04-17
ISBN 10: 1475724403
ISBN 13: 9781475724400
Language: EN, FR, DE, ES & NL

The Nature of Statistical Learning Theory Book Review:

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability.

Hands On Machine Learning with R

Hands On Machine Learning with R
Author: Brad Boehmke,Brandon M. Greenwell
Publsiher: CRC Press
Total Pages: 456
Release: 2019-11-15
ISBN 10: 1000730433
ISBN 13: 9781000730432
Language: EN, FR, DE, ES & NL

Hands On Machine Learning with R Book Review:

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Statistical Learning and Data Sciences

Statistical Learning and Data Sciences
Author: Alexander Gammerman,Vladimir Vovk,Harris Papadopoulos
Publsiher: Springer
Total Pages: 444
Release: 2015-04-02
ISBN 10: 3319170910
ISBN 13: 9783319170916
Language: EN, FR, DE, ES & NL

Statistical Learning and Data Sciences Book Review:

This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015. The 36 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on statistical learning and its applications, conformal prediction and its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.

Artificial Intelligence Applications and Innovations

Artificial Intelligence Applications and Innovations
Author: Lazaros Iliadis,Ilias Maglogiannis,Harris Papadopoulos,Spyros Sioutas,Christos Makris
Publsiher: Springer
Total Pages: 352
Release: 2014-09-15
ISBN 10: 3662447223
ISBN 13: 9783662447222
Language: EN, FR, DE, ES & NL

Artificial Intelligence Applications and Innovations Book Review:

This book constitutes the refereed proceedings of four AIAI 2014 workshops, co-located with the 10th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2014, held in Rhodes, Greece, in September 2014: the Third Workshop on Intelligent Innovative Ways for Video-to-Video Communications in Modern Smart Cities, IIVC 2014; the Third Workshop on Mining Humanistic Data, MHDW 2014; the Third Workshop on Conformal Prediction and Its Applications, CoPA 2014; and the First Workshop on New Methods and Tools for Big Data, MT4BD 2014. The 36 revised full papers presented were carefully reviewed and selected from numerous submissions. They cover a large range of topics in basic AI research approaches and applications in real world scenarios.

Conformal and Probabilistic Prediction with Applications

Conformal and Probabilistic Prediction with Applications
Author: Alexander Gammerman,Zhiyuan Luo,Jesús Vega,Vladimir Vovk
Publsiher: Springer
Total Pages: 229
Release: 2016-04-16
ISBN 10: 331933395X
ISBN 13: 9783319333953
Language: EN, FR, DE, ES & NL

Conformal and Probabilistic Prediction with Applications Book Review:

This book constitutes the refereed proceedings of the 5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016, held in Madrid, Spain, in April 2016. The 14 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 23 submissions and cover topics on theory of conformal prediction; applications of conformal prediction; and machine learning.

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
Author: Rani, Geeta,Tiwari, Pradeep Kumar
Publsiher: IGI Global
Total Pages: 586
Release: 2020-10-16
ISBN 10: 1799827437
ISBN 13: 9781799827436
Language: EN, FR, DE, ES & NL

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Book Review:

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Author: Christopher M. Bishop
Publsiher: Springer
Total Pages: 738
Release: 2016-08-23
ISBN 10: 9781493938438
ISBN 13: 1493938436
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Learning Book Review:

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Nonlinear Signal and Image Processing

Nonlinear Signal and Image Processing
Author: Kenneth E. Barner,Gonzalo R. Arce
Publsiher: CRC Press
Total Pages: 560
Release: 2003-11-24
ISBN 10: 0203010418
ISBN 13: 9780203010419
Language: EN, FR, DE, ES & NL

Nonlinear Signal and Image Processing Book Review:

Nonlinear signal and image processing methods are fast emerging as an alternative to established linear methods for meeting the challenges of increasingly sophisticated applications. Advances in computing performance and nonlinear theory are making nonlinear techniques not only viable, but practical. This book details recent advances in nonl

Source Separation and Machine Learning

Source Separation and Machine Learning
Author: Jen-Tzung Chien
Publsiher: Academic Press
Total Pages: 384
Release: 2018-11-01
ISBN 10: 0128045779
ISBN 13: 9780128045770
Language: EN, FR, DE, ES & NL

Source Separation and Machine Learning Book Review:

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems

Measures of Complexity

Measures of Complexity
Author: Vladimir Vovk,Harris Papadopoulos,Alexander Gammerman
Publsiher: Springer
Total Pages: 399
Release: 2015-09-03
ISBN 10: 3319218522
ISBN 13: 9783319218526
Language: EN, FR, DE, ES & NL

Measures of Complexity Book Review:

This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik–Chervonenkis) guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, classification, algorithmic statistics, and pattern recognition. The contributors are leading scientists in domains such as statistics, mathematics, and theoretical computer science, and the book will be of interest to researchers and graduate students in these domains.

Advances in Artificial Intelligence

Advances in Artificial Intelligence
Author: Cyril Goutte,Xiaodan Zhu
Publsiher: Springer Nature
Total Pages: 572
Release: 2020-05-05
ISBN 10: 3030473589
ISBN 13: 9783030473587
Language: EN, FR, DE, ES & NL

Advances in Artificial Intelligence Book Review:

This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during May 13–15, 2020. The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium papers were carefully reviewed and selected from a total of 175 submissions. The selected papers cover a wide range of topics, including machine learning, pattern recognition, natural language processing, knowledge representation, cognitive aspects of AI, ethics of AI, and other important aspects of AI research.

Empirical Inference

Empirical Inference
Author: Bernhard Schölkopf,Zhiyuan Luo,Vladimir Vovk
Publsiher: Springer Science & Business Media
Total Pages: 287
Release: 2013-12-11
ISBN 10: 3642411363
ISBN 13: 9783642411366
Language: EN, FR, DE, ES & NL

Empirical Inference Book Review:

This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection. These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning.

Introduction to Fire Safety Management

Introduction to Fire Safety Management
Author: Andrew Furness,Martin Muckett
Publsiher: Routledge
Total Pages: 419
Release: 2007
ISBN 10: 0750680687
ISBN 13: 9780750680684
Language: EN, FR, DE, ES & NL

Introduction to Fire Safety Management Book Review:

Andrew Furness and Martin Muckett give an introduction to all areas of fire safety management, including the legal framework, causes and prevention of fire and explosions, fire protection measures, fire risk assessment, and fire investigation. Fire safety is not treated as an isolated area but linked into an effective health and safety management system. Introduction to Fire Safety Management has been developed for the NEBOSH Certificate in Fire Safety and Risk Management and is also suitable for other NVQ level 3 and 4 fire safety courses. The text is highly illustrated in full colour, easy to read and supported by checklists, report forms and record sheets. This practical approach makes the book a valuable reference for health and safety professionals, fire officers, facility managers, safety reps, managers, supervisors and HR personnel in companies, as well as fire safety engineers, architects, construction managers and emergency fire services personnel. Andrew Furness CFIOSH, GIFireE, Dip2OSH, MIIRSM, MRSH, is Managing Director of Salvus Consulting Limited who specialise in Fire Safety. He was the chairman of the NEBOSH / IOSH working party that developed the NEBOSH Fire Safety and Risk Management certificate. Martin Muckett MA, MBA, CMIOSH, MIFireE, Dip2OSH, former Principal Health and Safety Advisor to The Fire Service Inspectorate and Principal Fire Safety Officer, Martin is currently Salvus Consulting Limited's Senior Fire Safety Trainer / Consultant. * Fully covers the syllabus for the NEBOSH Certificate in Fire Safety and Risk Management * Student-friendly presentation in full colour packed with illustrations and photographs * Includes a summary of legislation relevant to fire safety, ideal as a reference for students as well as practitioners

Pattern Recognition

Pattern Recognition
Author: Sergios Theodoridis,Konstantinos Koutroumbas
Publsiher: Elsevier
Total Pages: 689
Release: 2003-05-15
ISBN 10: 9780080513621
ISBN 13: 008051362X
Language: EN, FR, DE, ES & NL

Pattern Recognition Book Review:

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms. *Approaches pattern recognition from the designer's point of view *New edition highlights latest developments in this growing field, including independent components and support vector machines, not available elsewhere *Supplemented by computer examples selected from applications of interest