Rapid advances in the technologies of the Fourth Industrial Revolution pose a critical challenge to large, complex organizations both in the private and public sectors. The national defense ecosystem is no exception. The US Department of Defense has launched a number of efforts directed towards fostering creative and entrepreneurial problem solving, while evolving better practices to absorb external innovations. Defense Technological Innovation describes the emerging paradigm for innovation at the US Department of Defense, and the consequential impacts on its stakeholders. Leveraging a combination of prior research, archival data, first-person observations and interviews, the authors identify practices and themes characterizing the key trends in defense innovation, describe current organizational approaches and practices, and develop a theoretical framework that elucidates the competencies required to underwrite defense innovation objectives. The findings therein are relevant to any large, technology-driven organization contending with the implications of rapid change in the high-tech landscape. Scholars interested in the broad areas of technology innovation, national security, defense policy, and organizational change now have a book that addresses the problem of defense innovation. It will also appeal to scholars and policymakers in strategy, security studies, international affairs, and policy.

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.

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.