Now Publishers Products

All products from this brand (14 total)

53261834
Arabic is ranked as the seventh largest language on the Internet but it has also been the fastest growing language in the last decade in terms of users. At this rate of growth, Arabic users should have the fourth largest user population on the Internet by
Copula Modeling explores the copula approach for econometrics modeling of joint parametric distributions. Copula Modeling demonstrates that practical implementation and estimation is relatively straightforward despite the complexity of its theoretical foundations. An attractive feature of parametrically specific copulas is that estimation and inference are based on standard maximum likelihood procedures. Thus, copulas can be estimated using desktop econometric software. This offers a substantial advantage of copulas over recently proposed simulation-based approaches to joint modeling. Copulas are useful in a variety of modeling situations including financial markets, actuarial science, and microeconometrics modeling. Copula Modeling provides practitioners and scholars with a useful guide to copula modeling with a focus on estimation and misspecification. The authors cover important theoretical foundations. Throughout, the authors use Monte Carlo experiments and simulations to demonstrate copula properties
Credibility in Information Retrieval presents a detailed analysis of existing credibility models from different information seeking research areas, with a focus on the Web and its pervasive social component. It shows that there is a very rich body of work
Data Infrastructure for Medical Research
Natural Language Processing as a Foundation of the Semantic Web argues that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web, in several different ways, and whether its advocates realise this or not. Chiefly, it argues, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels based on lower level empirical computations over usage. The claim being made is definitely not logic-bad, NLP-good in any simple-minded way, but that the SW will be a fascinating interaction of these two methodologies, like the WWW (which, as the authors explain, has been a fruitful field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite resource description framework (RDF) knowledge stores for the SW from existing WWW (unstructured) text databases, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. It is also assumed here that, whatever the limitations on current SW representational power drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable. Natural Language Processing as a Foundation of the Semantic Web will appeal to researchers, practitioners and anyone with an interest in NLP, the philosophy of language, cognitive science, the Semantic Web and Web Science generally, as well as providing a magisterial and controversial overview of the history of artificial intelligence
Observability of Hybrid Dynamical Systems
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well-known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.
Scheduling in a stochastic network is an issue in many applications such as the workflow on production shop floors, automated service operations on internet platforms, traffic coordination in guidepath-based transport systems, and resource allocation in multi-core computer architectures. It is an area of the modern scheduling theory with very limited results. To a large extent, the lack of results is due to the intricacies that arise from the blocking and deadlocking effects that take place in these networks which prevent analysis using classical modeling frameworks. These type of scheduling problems can be resolved by breaking them into a supervisory control problem that seeks to prevent the deadlock formation in the underlying resource allocation dynamics, and a scheduling problem formulated on the admissible subspace to be defined by the adopted supervisory control policy. Logical Control of Complex Resource Allocation Systems provides a comprehensive tutorial on solutions to the supervisory control problem. The reader is shown results that are theoretically rigorous yet offer rich practical applications. These resource allocation techniques have major implications for many modern-day and future applications. They also point the way forward for the necessary further research to successfully solve the scheduling problems in such systems. Logical Control of Complex Resource Allocation Systems is a comprehensive introduction for students, researchers, and practitioners into a significant development at the core of many control systems.
Source Coding is the first part of the two-part monograph Fundamentals of Source and Video Coding by Wiegand and Schwarz. It is devoted to the fundamental subject of source coding. Source Coding is a standalone text and also forms the basis for the second part, which describes the application of sources coding techniques to video coding. Based on a simple and accessible presentation of the fundamentals of information and rate distortion theory, the authors describe the subjects of entropy coding and quantization as well as predictive and transform coding. All relevant source coding results that are required for the understanding of today's video compression algorithms are established. The emphasis is on source coding techniques that have become relevant for video coding in recent years. To illustrate the concepts and efficiency of the basic sources coding techniques, the authors provide numerous examples and experimental results for simple model sources. In addition to widely known results, the text also offers some elements that are new or rarely covered in references on source coding today, which include: Huffman coding for variable-length symbol sequences and PIPE coding, scalar quantization in combination with advanced entropy coding techniques, a simple model for the rate distortion performance of entropy-constrained scalar quantization for Gaussian sources that is valid over the entire bit rate range, a proof for the optimality of the Karhunen-Love transform for Gaussian sources. Source Coding is suitable as a primary text for courses on this subject. It can also be used as a resource for teaching and as a comprehensive reference for professional engineers and academic researches.
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate nonsmooth norms. Optimization with Sparsity-Inducing Penalties presents optimization tools and techniques dedicated to such sparsity-inducing penalties from a general perspective. It covers proximal methods, block-coordinate descent, reweighted ?2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provides an extensive set of experiments to compare various algorithms from a computational point of view. The presentation of Optimization with Sparsity-Inducing Penalties is essentially based on existing literature, but the process of constructing a general framework leads naturally to new results, connections and points of view. It is an ideal reference on the topic for anyone working in machine learning and related areas.
Temporal dynamics and how they impact upon various components of information retrieval (IR) systems have received a lot of attention in the last decade. In particular, the study of relevance in information retrieval can now be framed within the so-called
The Law and Economics of Public Health synthesizes the empirical research findings on the relationship between law and the public's health that are found scattered in different literature ranging from economic journals to medical journals, journals on add
The Role of Stakeholders in Corporate Governance: A View from Accounting Research