Conformal Prediction for Reliable Machine Learning

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  • Author : Vineeth Balasubramanian
  • Publisher : Newnes
  • Pages : 334 pages
  • ISBN : 0124017150
  • Rating : /5 from reviews
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Download or Read online Conformal Prediction for Reliable Machine Learning full in PDF, ePub and kindle. this book written by Vineeth Balasubramanian and published by Newnes which was released on 23 April 2014 with total page 334 pages. We cannot guarantee that Conformal Prediction for Reliable Machine Learning book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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
  • Publisher : Newnes
  • Release : 23 April 2014
GET THIS BOOK Conformal Prediction for Reliable Machine Learning

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

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  • Publisher : Springer Science & Business Media
  • Release : 22 March 2005
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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

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GET THIS BOOK Statistical Learning and Data Sciences

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  • Release : 15 September 2014
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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

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  • Publisher : Springer
  • Release : 16 April 2016
GET THIS BOOK Conformal and Probabilistic Prediction with Applications

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  • Publisher : Springer
  • Release : 03 September 2013
GET THIS BOOK Artificial Intelligence Applications and Innovations

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  • Publisher : Springer Nature
  • Release : 03 October 2019
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  • Publisher : Springer
  • Release : 01 September 2014
GET THIS BOOK Machine Learning and Knowledge Discovery in Databases

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.

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GET THIS BOOK Dynamic Data Driven Applications Systems

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.

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  • Publisher : Springer Nature
  • Release : 05 June 2020
GET THIS BOOK Information Processing and Management of Uncertainty in Knowledge Based Systems

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;

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  • Publisher : Springer Nature
  • Release : 29 June 2020
GET THIS BOOK Statistical Learning from a Regression Perspective

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GET THIS BOOK Measures of Complexity

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GET THIS BOOK Scalable Uncertainty Management

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