Conformal Prediction for Reliable Machine Learning

"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"--

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  • Author : Vineeth Balasubramanian
  • Publisher : Morgan Kaufmann
  • Pages : 298 pages
  • ISBN : 9780123985378
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKConformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning
  • Author : Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
  • Publisher : Morgan Kaufmann
  • Release : 24 May 2022
GET THIS BOOKConformal Prediction for Reliable Machine Learning

"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.

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 BOOKConformal 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

Algorithmic Learning in a Random World

Algorithmic Learning in a Random World
  • Author : Vladimir Vovk,Alexander Gammerman,Glenn Shafer
  • Publisher : Springer Science & Business Media
  • Release : 22 March 2005
GET THIS BOOKAlgorithmic Learning in a Random World

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

Conformal and Probabilistic Prediction with Applications

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

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.

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  • Publisher : Springer Nature
  • Release : 05 June 2020
GET THIS BOOKInformation 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 Science & Business Media
  • Release : 27 July 2010
GET THIS BOOKDynamics of Machinery

Dynamic loads and undesired oscillations increase with higher speed of machines. At the same time, industrial safety standards require better vibration reduction. This book covers model generation, parameter identification, balancing of mechanisms, torsional and bending vibrations, vibration isolation, and the dynamic behavior of drives and machine frames as complex systems. Typical dynamic effects, such as the gyroscopic effect, damping and absorption, shocks, resonances of higher order, nonlinear and self-excited vibrations are explained using practical examples. These include manipulators, flywheels, gears,

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  • Author : Alexander Gammerman,Vladimir Vovk,Harris Papadopoulos
  • Publisher : Springer
  • Release : 02 April 2015
GET THIS BOOKStatistical Learning and Data Sciences

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.

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  • Publisher : "O'Reilly Media, Inc."
  • Release : 30 November 2020
GET THIS BOOKIntroducing MLOps

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those

Advances and Trends in Artificial Intelligence. From Theory to Practice

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  • Publisher : Springer
  • Release : 28 June 2019
GET THIS BOOKAdvances and Trends in Artificial Intelligence. From Theory to Practice

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

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  • Publisher : Springer
  • Release : 15 September 2014
GET THIS BOOKArtificial Intelligence Applications and Innovations

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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  • Author : Brad Boehmke,Brandon M. Greenwell
  • Publisher : CRC Press
  • Release : 07 November 2019
GET THIS BOOKHands-On Machine Learning with R

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

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  • Publisher : John Wiley & Sons
  • Release : 11 March 2005
GET THIS BOOKProbability and Finance

Provides a foundation for probability based on game theory ratherthan measure theory. A strong philosophical approach with practicalapplications. Presents in-depth coverage of classical probability theory aswell as new theory.

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

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  • Publisher : IGI Global
  • Release : 16 October 2020
GET THIS BOOKHandbook of Research on Disease Prediction Through Data Analytics and Machine Learning

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

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GET THIS BOOKTheory of Machines

While writing the book,we have continuously kept in mind the examination requirments of the students preparing for U.P.S.C.(Engg. Services)and A.M.I.E.(I)examinations.In order to make this volume more useful for them,complete solutions of their examination papers up to 1975 have also been included.Every care has been taken to make this treatise as self-explanatory as possible.The subject matter has been amply illustrated by incorporating a good number of solved,

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  • Release : 18 November 2017
GET THIS BOOKAnomaly Detection Principles and Algorithms

This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which