GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

    In Wired for Speech, Clifford Nass and Scott Brave reveal how interactive voice technologies can readily and effectively tap into the automatic responses all speech--whether from human or machine--evokes. Wired for Speech demonstrates that people are voice-activated: we respond to voice technologies as we respond to actual people and behave as we would in any social situation. By leveraging this powerful finding, voice interfaces can truly emerge as the next frontier for efficient, user-friendly technology.Wired for Speech presents new theories and experiments and applies them to critical issues concerning how people interact with technology-based voices. It considers how people respond to a female voice in e-commerce (does stereotyping matter?), how a car's voice can promote safer driving (are happy cars better cars?), whether synthetic voices have personality and emotion (is sounding like a person always good?), whether an automated call center should apologize when it cannot understand a spoken request (To Err is Interface; To Blame, Complex), and much more. Nass and Brave's deep understanding of both social science and design, drawn from ten years of research at Nass's Stanford laboratory, produces results that often challenge conventional wisdom and common design practices. These insights will help designers and marketers build better interfaces, scientists construct better theories, and everyone gain better understandings of the future of the machines that speak with us.

    Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.