Conformal And Probabilistic Prediction And Applications 8 10 September 2021 Virtual
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Conformal and Probabilistic Prediction with Applications
| Author | : Alexander Gammerman |
| Publisher | : Springer |
| Total Pages | : 235 |
| Release | : 2016-04-16 |
| Genre | : Computers |
| ISBN | : 331933395X |
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.
Rough Sets
| Author | : JingTao Yao |
| Publisher | : Springer Nature |
| Total Pages | : 445 |
| Release | : 2022-11-10 |
| Genre | : Computers |
| ISBN | : 3031212444 |
This book constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2022, held in Suzhou, China, in November 2022. The 28 full papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Invited papers, IRSS President Forum; rough set theory and applications; granular computing and applications; classification and deep learning; conceptual knowledge discovery and machine learning based on three-way decisions and granular computing; uncertainty in three-way decisions; granular computing, and data science.
Healthcare Solutions Using Machine Learning and Informatics
| Author | : Punit Gupta |
| Publisher | : CRC Press |
| Total Pages | : 271 |
| Release | : 2022-10-21 |
| Genre | : Medical |
| ISBN | : 1000767205 |
Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence. The wide variety of topics covered include: Artificial Intelligence in healthcare Machine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19 Big data analytics solutions for healthcare data processing Reliable biomedical applications using AI models Intelligent IoT in healthcare The book explains fundamental concepts as well as the advanced use cases, illustrating how to apply emerging technologies such as machine learning, AI models, and data informatics into practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academicians and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain.
Algorithmic Learning in a Random World
| Author | : Vladimir Vovk |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 344 |
| Release | : 2005-03-22 |
| Genre | : Computers |
| ISBN | : 9780387001524 |
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.
Conformal Prediction for Reliable Machine Learning
| Author | : Vineeth Balasubramanian |
| Publisher | : Newnes |
| Total Pages | : 323 |
| Release | : 2014-04-23 |
| Genre | : Computers |
| ISBN | : 0124017150 |
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
Introduction to the Scenario Approach
| Author | : Marco C. Campi |
| Publisher | : SIAM |
| Total Pages | : 121 |
| Release | : 2018-11-15 |
| Genre | : Mathematics |
| ISBN | : 1611975433 |
This book is about making decisions driven by experience. In this context, a scenario is an observation that comes from the environment, and scenario optimization refers to optimizing decisions over a set of available scenarios. Scenario optimization can be applied across a variety of fields, including machine learning, quantitative finance, control, and identification. This concise, practical book provides readers with an easy access point to make the scenario approach understandable to nonexperts, and offers an overview of various decision frameworks in which the method can be used. It contains numerous examples and diverse applications from a broad range of domains, including systems theory, control, biomedical engineering, economics, and finance. Practitioners can find "easy-to-use recipes," while theoreticians will benefit from a rigorous treatment of the theoretical foundations of the method, making it an excellent starting point for scientists interested in doing research in this field. Introduction to the Scenario Approach will appeal to scientists working in optimization, practitioners working in myriad fields involving decision-making, and anyone interested in data-driven decision-making.
Scalable Uncertainty Management
| Author | : Florence Dupin de Saint-Cyr |
| Publisher | : Springer Nature |
| Total Pages | : 374 |
| Release | : 2022-10-14 |
| Genre | : Computers |
| ISBN | : 3031188438 |
This book constitutes the refereed proceedings of the 15th International Conference on Scalable Uncertainty Management, SUM 2022, which was held in Paris, France, in October 2022. The 19 full and 4 short papers presented in this volume were carefully reviewed and selected from 25 submissions. Besides that, the book also contains 3 abstracts of invited talks and 2 tutorial papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis. The chapter "Defining and Enforcing Descriptive Accuracy in Explanations: the Case of Probabilistic Classifiers" is licensed under the terms of the Creative Commons Attribution 4.0 International License.
Manifold Learning Theory and Applications
| Author | : Yunqian Ma |
| Publisher | : CRC Press |
| Total Pages | : 410 |
| Release | : 2011-12-20 |
| Genre | : Business & Economics |
| ISBN | : 1466558873 |
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread
Automation and Artificial Intelligence in Radiation Oncology
| Author | : Savino Cilla |
| Publisher | : Frontiers Media SA |
| Total Pages | : 232 |
| Release | : 2022-11-16 |
| Genre | : Medical |
| ISBN | : 2832505945 |
