Learning from Data

Many industry experts consider unsupervised learning the next AI frontier, one that may hold the key to general artificial intelligence. Armed with the conceptual knowledge in this book, data scientis

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The

A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomple

Learning Analytics in the Classroom presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educat

Learning Analytics in the Classroom presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educat

Data is an important ingredient in any successful education system. Building and sustaining a data system are challenging tasks. Many countries around the world have spent significant resources but st

This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after hav

Reconstructive integral geometry has gained new interest due to the number of new problems arising through X-Ray, MRI, gamma and positron radiography, ultrasound, and other new imaging fields. Even th

A pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for).Today’s decision making can be reached via information (related to

This book provides a comprehensive description of activity learning with techniques, details on existing algorithms, and case studies of technique applications to sensor datasets. Each chapter is cons

If you are a JavaScript developer or someone who has basic knowledge of JavaScript, and want to explore its optimum ability, this fast-paced book is definitely for you. Programming logic is the only t

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, fact

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, suppor

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, suppor

This book focuses on the design of learning algorithms for evolving and time-changing data streams, specifically the adaptive sliding window algorithm (ADWIN) for change detection and value estimation

Cultural differences increasingly impact our everyday lives. Virtually none of us today interact exclusively with people who look, talk, and behave like we do. David Smith here offers an excellent gui

Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, thei

As demands for accountability by national and state authorities increase in breadth and depth, educators are caught in the squeeze if they do not have sufficient skills in data gathering and interpret

Designed to teach students to apply statistical methods to real problems (a universal need), Bruce Trumbo's concise new book teaches basic statistical principles through their application to real data

In this book Bion describes his use of the term 'alpha-function' to conceptualize how the data of emotional experience is processed and digested. This includes his thinking on 'contact barriers' and t