The kernel of any operating system is its most critical component. The remainder of the system depends upon a correctly functioning and reliable kernel for its operation. The purpose of this book is ...to show that the formal specification of kernels can be followed by a completely formal refinement process that leads to the extraction of executable code. The formal refinement process ensures that the code meets the specification in a precise sense. Two kernels are specified and refined. The first is small and of the kind often used in embedded and real-time systems. It closely resembles the one modelled in our Formal Models of Operating System Kernels. The second is a Separation Kernel, a microkernel architecture devised for cryptographic and other secure applications. Both kernels are refined to the point at which executable code can be extracted. Apart from documenting the process, including proofs, this book also shows how refinement of a realistically sized specification can be undertaken.
The purpose of this book is to show that the formal specification of kernels is not only possible but also necessary if operating systems are to achieve the levels of reliability and security that is ...demanded of them today. Specifications of a sequence of kernels of increasing complexity are included, acting as models to enable the designer to identify and reason about the properties of the design - thus making explicit that which is too often left implicit or even unknown. A considerable amount of reasoning is included, showing what can be inferred about a design, and in addition, essential properties of data structures and mechanisms are discussed and the properties of these proved. Also included as an essential aspect of the activity, are the interfaces to the hardware and the processes running on them. It is very easy to get bogged down in complexity issues when considering kernels, but this bookïs prescriptive rather than descriptive approach shows how the kernel of an operating system can affect both the reliability and performance of these systems in a clear and concise style.
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural ...networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms.The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and ...comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Predicting Structured Data BakIr, Gökhan; Hofmann, Thomas; Schölkopf, Bernhard ...
2007, 20070727, 2007-07-27
eBook
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional ...constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field. Contributors Yasemin Altun, Gökhan Bakir no dot over i, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daumé III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Pérez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schölkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston Gökhan Bakir no dot over i is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. Thomas Hofmann is a Director of Engineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of Computer Science at Brown University. Bernhard Schölkopf is Director of the Max Planck Institute for Biological Cybernetics and Professor at the Technical University Berlin. Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania. S. V. N. Vishwanathan is Senior Researcher in the Statistical Machine Learning Program, National ICT Australia with an adjunct appointment at the Research School for Information Sciences and Engineering, Australian National University.
