Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. 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.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. 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 celebration of Caribbean American Heritage Month in June, we are sharing a collection of books by Caribbean American and Caribbean authors that includes fiction, memoir, non-fiction, and history. Find a full collection of titles here.
On Monday, May 5, three Penguin Random House authors were honored with a Pulitzer Prize. Established in 1917, the Pulitzer Prizes are the most prestigious awards in American letters. To date, PRH has 143 Pulitzer Prize winners, including William Faulkner, Eudora Welty, Josh Steinbeck, Ron Chernow, Anne Applebaum, Colson Whitehead, and many more. Take a look at our 2025 Pulitzer Prize
In June, we celebrate Black Music Appreciation month and the influential Black musicians and artists who have brought entertainment to their communities and beyond, and inspired others to make their own art. Collection of titles for Black Music Appreciation Month
In June we celebrate Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, and Asexual + (LGBTQIA+) Pride Month, which honors the 1969 Stonewall riots in Manhattan. Pride Month is a time to both celebrate the accomplishments of those in the LGBTQ+ community and recognize the ongoing struggles faced by many across the world who wish to live
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