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Cost-Sensitive Machine Learning - - Bog - Taylor & Francis Inc - Plusbog.dk

Cost-Sensitive Machine Learning - - Bog - Taylor & Francis Inc - Plusbog.dk

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: - Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation, and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

DKK 958.00
3

Model-Based Machine Learning - John Winn - Bog - Taylor & Francis Inc - Booktok.dk

Model-Based Machine Learning - John Winn - Bog - Taylor & Francis Inc - Booktok.dk

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: - Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. - Explains machine learning concepts as they arise in real-world case studies. - Shows how to diagnose, understand and address problems with machine learning systems. - Full source code available, allowing models and results to be reproduced and explored. - Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

DKK 720.00
4

The Resilience Machine - - Bog - Taylor & Francis Inc - Plusbog.dk

The Resilience Machine - - Bog - Taylor & Francis Inc - Plusbog.dk

We live in a time where environmental pressures, social inequities and political derision are the backdrop of everyday life, and where resilience has become a routine prescription for coping with the conditions of modern existence. Drawing an analogy to Harvey Molotch’s urban growth machine, this book explores different narratives of resilience and their policy and practice manifestations for cities, citizens and communities. It expands on the metaphor of the machine to show how resilience can be better understood as an assemblage. Bringing together authors from multiple disciplines and different parts of the world, the book unmasks the often invisible effects of resilience strategies by examining ways in which neoliberal mentalities are fed through the rhetoric of resilience practices, policies and development projects. The contributing essays provide provocative accounts of several areas of inquiry, including biopolitics and smart bodies, resilient cities and communities, urban planning and disaster management, justice and vulnerability, and resistance to resilience. Holding out hope for critical potentials in ‘resilience,’ The Resilience Machine proposes to move beyond mechanisms of adaptation and into imagining what resilient life could look like in a more just, equitable and democratic world. The Resilience Machine is a current, vital addition to resilience, community and urban scholarship.

DKK 514.00
4