Skip to main content

Conformal Prediction For Reliable Machine Learning

In Order to Read Online or Download Conformal Prediction For Reliable Machine Learning Full eBooks in PDF, EPUB, Tuebl and Mobi you need to create a Free account. Get any books you like and read everywhere you want. Fast Download Speed ~ Commercial & Ad Free. We cannot guarantee that every book is in the library!

Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning Book
Author : Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Publisher : Morgan Kaufmann
Release : 2014
ISBN : 9780123985378
Language : En, Es, Fr & De

GET BOOK

Book Description :

"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"--

Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning Book
Author : Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Publisher : Newnes
Release : 2014-04-23
ISBN : 0124017150
Language : En, Es, Fr & De

GET BOOK

Book Description :

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

Statistical Learning and Data Sciences

Statistical Learning and Data Sciences Book
Author : Alexander Gammerman,Vladimir Vovk,Harris Papadopoulos
Publisher : Springer
Release : 2015-04-02
ISBN : 3319170910
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Conformal and Probabilistic Prediction with Applications

Conformal and Probabilistic Prediction with Applications Book
Author : Alexander Gammerman,Zhiyuan Luo,Jesús Vega,Vladimir Vovk
Publisher : Springer
Release : 2016-04-16
ISBN : 331933395X
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Artificial Intelligence Applications and Innovations

Artificial Intelligence Applications and Innovations Book
Author : Lazaros Iliadis,Ilias Maglogiannis,Harris Papadopoulos,Spyros Sioutas,Christos Makris
Publisher : Springer
Release : 2014-09-15
ISBN : 3662447223
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Advances and Trends in Artificial Intelligence From Theory to Practice

Advances and Trends in Artificial Intelligence  From Theory to Practice Book
Author : Franz Wotawa,Gerhard Friedrich,Ingo Pill,Roxane Koitz-Hristov,Moonis Ali
Publisher : Springer
Release : 2019-06-28
ISBN : 3030229998
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Runtime Verification

Runtime Verification Book
Author : Bernd Finkbeiner,Leonardo Mariani
Publisher : Springer Nature
Release : 2019-10-03
ISBN : 3030320790
Language : En, Es, Fr & De

GET BOOK

Book Description :

This book constitutes the refereed proceedings of the 19th International Conference on Runtime Verification, RV 2019, held in Porto, Portugal, in October 2019. The 25 regular papers presented in this book were carefully reviewed and selected from 38 submissions. The RV conference is concerned with all aspects of monitoring and analysis of hardware, software and more general system executions. Runtime verification techniques are lightweight techniques to assess system correctness, reliability, and robustness; these techniques are significantly more powerful and versatile than conventional testing, and more practical than exhaustive formal verification. Chapter “Assumption-Based Runtime Verification with Partial Observability and Resets” and chapter “NuRV: a nuXmv Extension for Runtime Verification“ are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases Book
Author : Toon Calders,Floriana Esposito,Eyke Hüllermeier,Rosa Meo
Publisher : Springer
Release : 2014-09-01
ISBN : 3662448513
Language : En, Es, Fr & De

GET BOOK

Book Description :

This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.

Dynamic Data Driven Applications Systems

Dynamic Data Driven Applications Systems Book
Author : Frederica Darema,Erik Blasch,Sai Ravela,Alex Aved
Publisher : Springer Nature
Release : 2020-11-02
ISBN : 3030617254
Language : En, Es, Fr & De

GET BOOK

Book Description :

This book constitutes the refereed proceedings of the Third International Conference on Dynamic Data Driven Application Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020. The 21 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They cover topics such as: digital twins; environment cognizant adaptive-planning systems; energy systems; materials systems; physics-based systems analysis; imaging methods and systems; and learning systems.

Statistical Learning from a Regression Perspective

Statistical Learning from a Regression Perspective Book
Author : Richard A. Berk
Publisher : Springer Nature
Release : 2020-06-29
ISBN : 3030401898
Language : En, Es, Fr & De

GET BOOK

Book Description :

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of “big data” on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.

Measures of Complexity

Measures of Complexity Book
Author : Vladimir Vovk,Harris Papadopoulos,Alexander Gammerman
Publisher : Springer
Release : 2015-09-03
ISBN : 3319218522
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Scalable Uncertainty Management

Scalable Uncertainty Management Book
Author : Nahla Ben Amor,Benjamin Quost,Martin Theobald
Publisher : Springer Nature
Release : 2019-12-02
ISBN : 3030355144
Language : En, Es, Fr & De

GET BOOK

Book Description :

This book constitutes the refereed proceedings of the 13th International Conference on Scalable Uncertainty Management, SUM 2019, which was held in Compiègne, France, in December 2019. The 25 full, 4 short, 4 tutorial, 2 invited keynote papers presented in this volume were carefully reviewed and selected from 44 submissions. The conference is dedicated to the management of large amounts of complex, uncertain, incomplete, or inconsistent information. New approaches have been developed on imprecise probabilities, fuzzy set theory, rough set theory, ordinal uncertainty representations, or even purely qualitative models.

Information Processing and Management of Uncertainty in Knowledge Based Systems

Information Processing and Management of Uncertainty in Knowledge Based Systems Book
Author : Marie-Jeanne Lesot,Susana Vieira,Marek Z. Reformat,João Paulo Carvalho,Anna Wilbik,Bernadette Bouchon-Meunier,Ronald R. Yager
Publisher : Springer Nature
Release : 2020-06-05
ISBN : 3030501469
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Runtime Verification

Runtime Verification Book
Author : Lu Feng,Dana Fisman
Publisher : Springer Nature
Release : 2021-10-05
ISBN : 3030884945
Language : En, Es, Fr & De

GET BOOK

Book Description :

This book constitutes the refereed proceedings of the 21st International Conference on Runtime Verification, RV 2021, held virtually during October 11-14, 2021. The 11 regular papers and 7 short/tool/benchmark papers presented in this book were carefully reviewed and selected from 40 submissions. Also included is one tutorial paper. The RV conference is concerned with all aspects of monitoring and analysis of hardware, software and more general system executions.

Algorithmic Learning in a Random World

Algorithmic Learning in a Random World Book
Author : Vladimir Vovk,Alexander Gammerman,Glenn Shafer
Publisher : Springer Science & Business Media
Release : 2005-03-22
ISBN : 9780387001524
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Computational Intelligence in Data Mining Volume 3

Computational Intelligence in Data Mining   Volume 3 Book
Author : Lakhmi C. Jain,Himansu Sekhar Behera,Jyotsna Kumar Mandal,Durga Prasad Mohapatra
Publisher : Springer
Release : 2014-12-11
ISBN : 8132222024
Language : En, Es, Fr & De

GET BOOK

Book Description :

The contributed volume aims to explicate and address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases Book
Author : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publisher : Springer Nature
Release : 2021-02-24
ISBN : 3030676641
Language : En, Es, Fr & De

GET BOOK

Book Description :

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Fuzzy Logic and Soft Computing Applications

Fuzzy Logic and Soft Computing Applications Book
Author : Alfredo Petrosino,Vincenzo Loia,Witold Pedrycz
Publisher : Springer
Release : 2017-02-06
ISBN : 3319529625
Language : En, Es, Fr & De

GET BOOK

Book Description :

This book constitutes the proceedings of the 11th International Workshop on Fuzzy Logic and Applications, WILF 2016, held in Naples, Italy, in December 2016. The 22 revised full papers presented together with 2 invited lectures were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy measures and transforms; granularity and multi-logics, clustering and learning; knowledge systems; and soft computing and applications.

Advanced Intelligent Systems for Sustainable Development AI2SD 2020

Advanced Intelligent Systems for Sustainable Development  AI2SD   2020  Book
Author : Janusz Kacprzyk,Valentina E. Balas,Mostafa Ezziyyani
Publisher : Springer Nature
Release : 2022-03-11
ISBN : 3030906337
Language : En, Es, Fr & De

GET BOOK

Book Description :

This book publishes the best papers accepted and presented at the 3rd edition of the International Conference on Advanced Intelligent Systems for Sustainable Development Applied to Agriculture, Energy, Health, Environment, Industry, Education, Economy, and Security (AI2SD’2020). This conference is one of the biggest amalgamations of eminent researchers, students, and delegates from both academia and industry where the collaborators have an interactive access to emerging technology and approaches globally. In this book, readers find the latest ideas addressing technological issues relevant to all areas of the social and human sciences for sustainable development. Due to the nature of the conference with its focus on innovative ideas and developments, the book provides the ideal scientific and brings together very high-quality chapters written by eminent researchers from different disciplines, to discover the most recent developments in scientific research.

Advances in Machine Learning Research and Application 2013 Edition

Advances in Machine Learning Research and Application  2013 Edition Book
Author : Anonim
Publisher : ScholarlyEditions
Release : 2013-06-21
ISBN : 1481683195
Language : En, Es, Fr & De

GET BOOK

Book Description :

Advances in Machine Learning Research and Application: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Artificial Intelligence. The editors have built Advances in Machine Learning Research and Application: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Artificial Intelligence in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Advances in Machine Learning Research and Application: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.