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Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning
Theory, Adaptations and Applications

by Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk

  • Publisher : Newnes
  • Release : 2014-04-23
  • Pages : 334
  • ISBN : 0124017150
  • Language : En, Es, Fr & De
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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
Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings

by Alexander Gammerman,Vladimir Vovk,Harris Papadopoulos

  • Publisher : Springer
  • Release : 2015-04-02
  • Pages : 444
  • ISBN : 3319170910
  • Language : En, Es, Fr & De
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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
AIAI 2014 Workshops: CoPA, MHDW, IIVC, and MT4BD, Rhodes, Greece, September 19-21, 2014, Proceedings

by Lazaros Iliadis,Ilias Maglogiannis,Harris Papadopoulos,Spyros Sioutas,Christos Makris

  • Publisher : Springer
  • Release : 2014-09-15
  • Pages : 352
  • ISBN : 3662447223
  • Language : En, Es, Fr & De
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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
5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016, Proceedings

by Alexander Gammerman,Zhiyuan Luo,Jesús Vega,Vladimir Vovk

  • Publisher : Springer
  • Release : 2016-04-16
  • Pages : 229
  • ISBN : 331933395X
  • Language : En, Es, Fr & De
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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
A Book

by Rani, Geeta,Tiwari, Pradeep Kumar

  • Publisher : IGI Global
  • Release : 2020-10-16
  • Pages : 586
  • ISBN : 1799827437
  • Language : En, Es, Fr & De
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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.

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings
A Book

by Nicolas Papernot

  • Publisher : Unknown Publisher
  • Release : 2018
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as object recognition, autonomous systems, security diagnostics, and playing the game of Go. Machine learning is not only a new paradigm for building software and systems, it is bringing social disruption at scale. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical communitys understanding of the nature and extent of these vulnerabilities remains limited. In this thesis, I focus my study on the integrity of ML models. Integrity refers here to the faithfulness of model predictions with respect to an expected outcome. This property is at the core of traditional machine learning evaluation, as demonstrated by the pervasiveness of metrics such as accuracy among practitioners. A large fraction of ML techniques were designed for benign execution environments. Yet, the presence of adversaries may invalidate some of these underlying assumptions by forcing a mismatch between the distributions on which the model is trained and tested. As ML is increasingly applied and being relied on for decision-making in critical applications like transportation or energy, the models produced are becoming a target for adversaries who have a strong incentive to force ML to mispredict. I explore the space of attacks against ML integrity at test time. Given full or limited access to a trained model, I devise strategies that modify the test data to create a worst-case drift between the training and test distributions. The implications of this part of my research is that an adversary with very weak access to a system, and little knowledge about the ML techniques it deploys, can nevertheless mount powerful attacks against such systems as long as she has the capability of interacting with it as an oracle: i.e., send inputs of the adversarys choice and observe the ML prediction. This systematic exposition of the poor generalization of ML models indicates the lack of reliable confidence estimates when the model is making predictions far from its training data. Hence, my efforts to increase the robustness of models to these adversarial manipulations strive to decrease the confidence of predictions made far from the training distribution. Informed by my progress on attacks operating in the black-box threat model, I first identify limitations to two defenses: defensive distillation and adversarial training. I then describe recent defensive efforts addressing these shortcomings. To this end, I introduce the Deep k-Nearest Neighbors classifier, which augments deep neural networks with an integrity check at test time. The approach compares internal representations produced by the deep neural network on test data with the ones learned on its training points. Using the labels of training points whose representations neighbor the test input across the deep neural networks layers, I estimate the nonconformity of the prediction with respect to the models training data. An application of conformal prediction methodology then paves the way for more reliable estimates of the models prediction credibility, i.e., how well the prediction is supported by training data. In turn, we distinguish legitimate test data with high credibility from adversarial data with low credibility. This research calls for future efforts to investigate the robustness of individual layers of deep neural networks rather than treating the model as a black-box. This aligns well with the modular nature of deep neural networks, which orchestrate simple computations to model complex functions. This also allows us to draw connections to other areas like interpretability in ML, which seeks to answer the question of: How can we provide an explanation for the model prediction to a human? Another by-product of this research direction is that I better distinguish vulnerabilities of ML models that are a consequence of the ML algorithms from those that can be explained by artifacts in the data.

Algorithmic Learning in a Random World

Algorithmic Learning in a Random World
A Book

by Vladimir Vovk,Alexander Gammerman,Glenn Shafer

  • Publisher : Springer Science & Business Media
  • Release : 2005-03-22
  • Pages : 324
  • ISBN : 9780387001524
  • Language : En, Es, Fr & De
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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.

Advances and Trends in Artificial Intelligence. From Theory to Practice

Advances and Trends in Artificial Intelligence. From Theory to Practice
32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings

by Franz Wotawa,Gerhard Friedrich,Ingo Pill,Roxane Koitz-Hristov,Moonis Ali

  • Publisher : Springer
  • Release : 2019-06-28
  • Pages : 865
  • ISBN : 3030229998
  • Language : En, Es, Fr & De
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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.

Decision Analytics Applications in Industry

Decision Analytics Applications in Industry
A Book

by P. K. Kapur,Gurinder Singh,Yury S. Klochkov,Uday Kumar

  • Publisher : Springer Nature
  • Release : 2020-05-27
  • Pages : 555
  • ISBN : 9811536430
  • Language : En, Es, Fr & De
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This book presents a range of qualitative and quantitative analyses in areas such as cybersecurity, sustainability, multivariate analysis, customer satisfaction, parametric programming, software reliability growth modeling, and blockchain technology, to name but a few. It also highlights integrated methods and practices in the areas of machine learning and genetic algorithms. After discussing applications in supply chains and logistics, cloud computing, six sigma, production management, big data analysis, satellite imaging, game theory, biometric systems, quality, and system performance, the book examines the latest developments and breakthroughs in the field of science and technology, and provides novel problem-solving methods. The themes discussed in the book link contributions by researchers and practitioners from different branches of engineering and management, and hailing from around the globe. These contributions provide scholars with a platform to derive maximum utility in the area of analytics by subscribing to the idea of managing business through system sciences, operations, and management. Managers and decision-makers can learn a great deal from the respective chapters, which will help them devise their own business strategies and find real-world solutions to complex industrial problems.

Government Reports Annual Index

Government Reports Annual Index
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1990
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Artificial Intelligence Abstracts Annual 1989

Artificial Intelligence Abstracts Annual 1989
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1990-04
  • Pages : 604
  • ISBN : 9780835226745
  • Language : En, Es, Fr & De
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A Directory of Computer Software Applications

A Directory of Computer Software Applications
Electrical & electronics engineering

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1978
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Index to IEEE Publications

Index to IEEE Publications
A Book

by Institute of Electrical and Electronics Engineers

  • Publisher : Unknown Publisher
  • Release : 1990
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Issues for 1973- cover the entire IEEE technical literature.

Government Reports Announcements

Government Reports Announcements
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1971
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Scientific and Technical Aerospace Reports

Scientific and Technical Aerospace Reports
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1971
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Dissertation Abstracts International

Dissertation Abstracts International
The sciences and engineering. B

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1987
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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International Aerospace Abstracts

International Aerospace Abstracts
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1968-05
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Progress in Applied Sciences, Engineering and Technology

Progress in Applied Sciences, Engineering and Technology
A Book

by Pei Long Xu,Hong Zong Si,Yi Qian Wang,Pin Wang

  • Publisher : Trans Tech Publications Ltd
  • Release : 2014-05-23
  • Pages : 5154
  • ISBN : 3038264806
  • Language : En, Es, Fr & De
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Collection of selected, peer reviewed papers from the 2014 International Conference on Materials Science and Computational Engineering (ICMSCE 2014), May 20-21, 2014, Qingdao, China. The 1116 papers are grouped as follows: I. Material Science, Chemical Engineering and Technologies, II. Electric material and Electronic Devices, III. Construction Materials, Architecture Science and Civil Engineering, IV. Industrial, Mechanical and Manufacturing Engineering, V. Power Engineering and Energy Supply, VI. Biological Engineering and Food Science, VII. Medicine and Health Engineering, VIII. Products Design and Simulation, Intelligent and Control Systems, IX. Signal Processing and Computer Aided Modeling and Design, X. Communications and Information Technology Applications, XI. Computational Science Technology, Algorithms, XII. Management, Economics, Business, Logistics and Engineering Management, XIII. Environmental Engineering and Resource Development, XIV. New Technologies in Engineering Education and Teaching

Index to Theses with Abstracts Accepted for Higher Degrees by the Universities of Great Britain and Ireland and the Council for National Academic Awards

Index to Theses with Abstracts Accepted for Higher Degrees by the Universities of Great Britain and Ireland and the Council for National Academic Awards
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2004
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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A Directory of Computer Software Applications--electrical and Electronics Engineering, 1970-Sept. 1978

A Directory of Computer Software Applications--electrical and Electronics Engineering, 1970-Sept. 1978
A Book

by United States. National Technical Information Service

  • Publisher : Unknown Publisher
  • Release : 1978
  • Pages : 115
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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