Download or read book Conformal Prediction for Reliable Machine Learning written by Vineeth Balasubramanian and published by Newnes. This book was released on 2014-04-23 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Algorithmic Learning in a Random World written by Vladimir Vovk and published by Springer Science & Business Media. This book was released on 2005-03-22 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Conformal and Probabilistic Prediction with Applications written by Alexander Gammerman and published by Springer. This book was released on 2016-04-16 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Practical Guide to Applied Conformal Prediction in Python written by Valery Manokhin and published by Packt Publishing Ltd. This book was released on 2023-12-20 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Elevate your machine learning skills using the Conformal Prediction framework for uncertainty quantification. Dive into unique strategies, overcome real-world challenges, and become confident and precise with forecasting. Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications Explore cutting-edge methods to measure and manage uncertainty in industry applications Understand how Conformal Prediction differs from traditional machine learning Book DescriptionIn the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.What you will learn The fundamental concepts and principles of conformal prediction Learn how conformal prediction differs from traditional ML methods Apply real-world examples to your own industry applications Explore advanced topics - imbalanced data and multi-class CP Dive into the details of the conformal prediction framework Boost your career as a data scientist, ML engineer, or researcher Learn to apply conformal prediction to forecasting and NLP Who this book is for Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.

Download or read book Probability and Finance written by Glenn Shafer and published by John Wiley & Sons. This book was released on 2005-02-25 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a foundation for probability based on game theory rather than measure theory. A strong philosophical approach with practical applications. Presents in-depth coverage of classical probability theory as well as new theory.

Download or read book Functional and High Dimensional Statistics and Related Fields written by Germán Aneiros and published by Springer Nature. This book was released on 2020-06-19 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field.

Download or read book Statistical Learning and Data Science written by Mireille Gettler Summa and published by CRC Press. This book was released on 2011-12-19 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor

Download or read book Dataset Shift in Machine Learning written by Joaquin Quinonero-Candela and published by MIT Press. This book was released on 2022-06-07 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama

Download or read book Cycles of Time written by Roger Penrose and published by Vintage. This book was released on 2011-09-06 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: From Nobel prize-winner Roger Penrose, this groundbreaking book is for anyone "who is interested in the world, how it works, and how it got here" (New York Journal of Books). Penrose presents a new perspective on three of cosmology’s essential questions: What came before the Big Bang? What is the source of order in our universe? And what cosmic future awaits us? He shows how the expected fate of our ever-accelerating and expanding universe—heat death or ultimate entropy—can actually be reinterpreted as the conditions that will begin a new “Big Bang.” He details the basic principles beneath our universe, explaining various standard and non-standard cosmological models, the fundamental role of the cosmic microwave background, the paramount significance of black holes, and other basic building blocks of contemporary physics. Intellectually thrilling and widely accessible, Cycles of Time is a welcome new contribution to our understanding of the universe from one of our greatest mathematicians and thinkers.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Algorithmic Foundations of Robotics XV written by Steven M. LaValle and published by Springer Nature. This book was released on 2022-12-14 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes significant recent research on robotic algorithms. It has been written by leading experts in the field. The 15th Workshop on the Algorithmic Foundations of Robotics (WAFR) was held on June 22–24, 2022, at the University of Maryland, College Park, Maryland. Each chapter represents an exciting state-of-the-art development in robotic algorithms that was presented at this 15th incarnation of WAFR. Different chapters combine ideas from a wide variety of fields, spanning and combining planning (for tasks, paths, motion, navigation, coverage, and patrol), computational geometry and topology, control theory, machine learning, formal methods, game theory, information theory, and theoretical computer science. Many of these papers explore new and interesting problems and problem variants that include human–robot interaction, planning and reasoning under uncertainty, dynamic environments, distributed decision making, multi-agent coordination, and heterogeneity.

Download or read book Machine Learning and Principles and Practice of Knowledge Discovery in Databases written by Irena Koprinska and published by Springer Nature. This book was released on 2023-01-30 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022. The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents the following workshops: Workshop on Data Science for Social Good (SoGood 2022) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022) Workshop on Explainable Knowledge Discovery in Data Mining (XKDD 2022) Workshop on Uplift Modeling (UMOD 2022) Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022) Workshop on Mining Data for Financial Application (MIDAS 2022) Workshop on Machine Learning for Cybersecurity (MLCS 2022) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022) Workshop on Data Analysis in Life Science (DALS 2022) Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022)

Download or read book Artificial Intelligence Applications and Innovations written by Harris Papadopoulos and published by Springer Science & Business Media. This book was released on 2010-09-21 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: The abundance of information and increase in computing power currently enable researchers to tackle highly complicated and challenging computational problems. Solutions to such problems are now feasible using advances and innovations from the area of Artificial Intelligence. The general focus of the AIAI conference is to provide insights on how Artificial Intelligence may be applied in real-world situations and serve the study, analysis and modeling of theoretical and practical issues. This volume contains papers selected for presentation at the 6th IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI 2010) and held in Larnaca, Cyprus, during October 6–7, 2010. IFIP AIAI 2010 was co-organized by the University of Cyprus and the Cyprus University of Technology and was sponsored by the Cyprus University of Technology, Frederick University and the Cyprus Tourism Organization. AIAI 2010 is the official conference of the WG12.5 “Artificial Intel- gence Applications” working group of IFIP TC12, the International Federation for Information Processing Technical Committee on Artificial Intelligence (AI). AIAI is a conference that grows in significance every year attracting researchers from different countries around the globe. It maintains high quality, standards and welcomes research papers describing technical advances and engineering and ind- trial applications of intelligent systems. AIAI 2010 was not confined to introducing how AI may be applied in real-life situations, but also included innovative methods, techniques, tools and ideas of AI expressed at the algorithmic or systemic level.

Download or read book Machine Learning Q and AI written by Sebastian Raschka and published by No Starch Press. This book was released on 2024-04-16 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field. If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about. Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises. WHAT'S INSIDE: FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts. WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing. PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more. You’ll also explore how to: • Manage the various sources of randomness in neural network training • Differentiate between encoder and decoder architectures in large language models • Reduce overfitting through data and model modifications • Construct confidence intervals for classifiers and optimize models with limited labeled data • Choose between different multi-GPU training paradigms and different types of generative AI models • Understand performance metrics for natural language processing • Make sense of the inductive biases in vision transformers If you’ve been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.

Download or read book Game Theoretic Foundations for Probability and Finance written by Glenn Shafer and published by John Wiley & Sons. This book was released on 2019-03-21 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Game-theoretic probability and finance come of age Glenn Shafer and Vladimir Vovk’s Probability and Finance, published in 2001, showed that perfect-information games can be used to define mathematical probability. Based on fifteen years of further research, Game-Theoretic Foundations for Probability and Finance presents a mature view of the foundational role game theory can play. Its account of probability theory opens the way to new methods of prediction and testing and makes many statistical methods more transparent and widely usable. Its contributions to finance theory include purely game-theoretic accounts of Ito’s stochastic calculus, the capital asset pricing model, the equity premium, and portfolio theory. Game-Theoretic Foundations for Probability and Finance is a book of research. It is also a teaching resource. Each chapter is supplemented with carefully designed exercises and notes relating the new theory to its historical context. Praise from early readers “Ever since Kolmogorov's Grundbegriffe, the standard mathematical treatment of probability theory has been measure-theoretic. In this ground-breaking work, Shafer and Vovk give a game-theoretic foundation instead. While being just as rigorous, the game-theoretic approach allows for vast and useful generalizations of classical measure-theoretic results, while also giving rise to new, radical ideas for prediction, statistics and mathematical finance without stochastic assumptions. The authors set out their theory in great detail, resulting in what is definitely one of the most important books on the foundations of probability to have appeared in the last few decades.” – Peter Grünwald, CWI and University of Leiden “Shafer and Vovk have thoroughly re-written their 2001 book on the game-theoretic foundations for probability and for finance. They have included an account of the tremendous growth that has occurred since, in the game-theoretic and pathwise approaches to stochastic analysis and in their applications to continuous-time finance. This new book will undoubtedly spur a better understanding of the foundations of these very important fields, and we should all be grateful to its authors.” – Ioannis Karatzas, Columbia University

Download or read book Algorithmic Learning in a Random World written by Vladimir Vovk and published by Springer Nature. This book was released on 2022-12-13 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described — conformal predictors — are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the latter being especially dangerous). The approach is still flexible enough to incorporate most of the existing powerful methods of machine learning. The book covers both key conformal predictors and the mathematical analysis of their properties. Algorithmic Learning in a Random World contains, in addition to proofs of validity, results about the efficiency of conformal predictors. The only assumption required for validity is that of "randomness" (the prediction algorithm is presented with independent and identically distributed examples); in later chapters, even the assumption of randomness is significantly relaxed. Interesting results about efficiency are established both under randomness and under stronger assumptions. Since publication of the First Edition in 2005 conformal prediction has found numerous applications in medicine and industry, and is becoming a popular machine-learning technique. This Second Edition contains three new chapters. One is about conformal predictive distributions, which are more informative than the set predictions produced by standard conformal predictors. Another is about the efficiency of ways of testing the assumption of randomness based on conformal prediction. The third new chapter harnesses conformal testing procedures for protecting machine-learning algorithms against changes in the distribution of the data. In addition, the existing chapters have been revised, updated, and expanded.

Download or read book Artificial Intelligence in Drug Discovery written by Nathan Brown and published by Royal Society of Chemistry. This book was released on 2020-11-12 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation.