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

Explains the significance of each card in the tarot deck and tells how to use the cards to find advice or predict the future

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.

In this off-beat book perfect for reading aloud, a Caldecott Honor winner shares the story of a duck who rides a bike with hilarious results. One day down on the farm, Duck got a wild idea. “I bet I could ride a bike,” he thought. He waddled over to where the boy parked his bike, climbed on, and began to ride. At first, he rode slowly and he wobbled a lot, but it was fun! Duck rode past Cow and waved to her. “Hello, Cow!” said Duck. “Moo,” said Cow. But what she thought was, “A duck on a bike? That’s the silliest thing I’ve ever seen!” And so, Duck rides past Sheep, Horse, and all the other barnyard animals. Suddenly, a group of kids ride by on their bikes and run into the farmhouse, leaving the bikes outside. Now ALL the animals can ride bikes, just like Duck! Praise for Duck on a Bike “Shannon serves up a sunny blend of humor and action in this delightful tale of a Duck who spies a red bicycle one day and gets “a wild idea” . . . Add to all this the abundant opportunity for youngsters to chime in with barnyard responses (“M-o-o-o”; “Cluck! Cluck!”), and the result is one swell read-aloud, packed with freewheeling fun.” —Publishers Weekly “Grab your funny bone—Shannon . . . rides again! . . . A “quackerjack” of a terrific escapade.” —Kirkus Reviews

Based upon ten case studies, Prediction explores how science-based predictions guide policy making and what this means in terms of global warming, biogenetically modifying organisms and polluting the environment with chemicals.

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

This book discusses in detail the basic algorithms of video compression that are widely used in modern video codec. The authors dissect complicated specifications and present material in a way that gets readers quickly up to speed by describing video compression algorithms succinctly, without going to the mathematical details and technical specifications. For accelerated learning, hybrid codec structure, inter- and intra- prediction techniques in MPEG-4, H.264/AVC, and HEVC are discussed together. In addition, the latest research in the fast encoder design for the HEVC and H.264/AVC is also included.

A potato and his eggplant nemesis struggle to find the perfect pants in this hilarious, heartwarming tale of forgiveness by bestselling Geisel-Award winning creator Laurie Keller. Potato is excited because today—for one day only— Lance Vance’s Fancy Pants Store is selling . . .POTATO PANTS! Potato rushes over early, but just as he’s about to walk in, something makes him stop. What could it be? Find out in this one-of-a-kind story about misunderstandings and forgiveness, and—of course—Potato Pants! A Christy Ottaviano Book This title has Common Core connections.

The Gap Between Weather and Climate Forecasting: Sub-seasonal to Seasonal Prediction is an ideal reference for researchers and practitioners across the range of disciplines involved in the science, modeling, forecasting and application of this new frontier in sub-seasonal to seasonal (S2S) prediction. It provides an accessible, yet rigorous, introduction to the scientific principles and sources of predictability through the unique challenges of numerical simulation and forecasting with state-of-science modeling codes and supercomputers. Additional coverage includes the prospects for developing applications to trigger early action decisions to lessen weather catastrophes, minimize costly damage, and optimize operator decisions. The book consists of a set of contributed chapters solicited from experts and leaders in the fields of S2S predictability science, numerical modeling, operational forecasting, and developing application sectors. The introduction and conclusion, written by the co-editors, provides historical perspective, unique synthesis and prospects, and emerging opportunities in this exciting, complex and interdisciplinary field. - Contains contributed chapters from leaders and experts in sub-seasonal to seasonal science, forecasting and applications - Provides a one-stop shop for graduate students, academic and applied researchers, and practitioners in an emerging and interdisciplinary field - Offers a synthesis of the state of S2S science through the use of concrete examples, enabling potential users of S2S forecasts to quickly grasp the potential for application in their own decision-making - Includes a broad set of topics, illustrated with graphic examples, that highlight interdisciplinary linkages

The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.