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Green Innovations for Industrial Development and Business Sustainability
Focusing on the business implications of green innovation, this book describes the sheer impact, spread, and opportunities arising every day, and how business leaders can implement green innovative practices today to realize tangible as well as intangible business advantages in the future. Green Innovations for Industrial Development and Business Sustainability: Models and Implementation Strategies works as a guide for green innovation that focuses on enterprise applications for those tasked with leveraging green practice adoption to enhance the productivity of their organization. The book offers the ability to understand the latest developments in green innovations and their business applic...
Learning Theory
This book constitutes the refereed proceedings of the 18th Annual Conference on Learning Theory, COLT 2005, held in Bertinoro, Italy in June 2005. The 45 revised full papers together with three articles on open problems presented were carefully reviewed and selected from a total of 120 submissions. The papers are organized in topical sections on: learning to rank, boosting, unlabeled data, multiclass classification, online learning, support vector machines, kernels and embeddings, inductive inference, unsupervised learning, generalization bounds, query learning, attribute efficiency, compression schemes, economics and game theory, separation results for learning models, and survey and prospects on open problems.
Entrepreneurial Innovations, Models, and Implementation Strategies for Industry 4.0
This book explores the link between entrepreneurship and innovation, providing an understanding of the latest developments in the field. It explores numerous challenges to entrepreneurship, such as failures and socio-economic issues, and presents concepts, models, and implementation strategies for Industry 4.0. Industry 4.0 presents unique challenges and unique opportunities to entrepreneurs. Social, techno, and women’s entrepreneurship, among other business sectors and domains, are discussed in this new context. This book highlights the strategies of successful start-up organizations, focusing on the most critical factors for starting a new enterprise in today’s business environment, an...
Boosting
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterio...
The Adoption and Effect of Artificial Intelligence on Human Resources Management
Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management.
Transitioning from Pediatric to Adult Care in Endocrinology
This unique handbook provides pediatric and adult endocrinologists and multidisciplinary clinical care providers a guide to transition from pediatric to adult care and an understanding of developmental and psychosocial issues of young adulthood and how they relate to healthcare and disease self-management. The handbook is divided into two parts. Part one describes transition interventions and novel strategies that can be integrated into routine care and gives practical considerations for transition processes from both the pediatric and adult perspectives, with applications across multiple endocrine conditions. Part two focuses on transition issues specific to common endocrine conditions – ...
Recent Advances in Internet of Things and Machine Learning
This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.
Machine Learning Challenges
This book constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Learning Theory
This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA, June 2006. The book presents 43 revised full papers together with 2 articles on open problems and 3 invited lectures. The papers cover a wide range of topics including clustering, un- and semi-supervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, and more.
