Exact and Approximate Modeling of Linear Systems: A Behavioral Approach elegantly introduces the behavioral approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose. This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation. Readers will find an exposition of a wide variety of modeling problems starting from observed data. The presented theory leads to algorithms that are implemented in C language and in MATLAB.
Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.
This industrially relevant resource covers all established and emerging analytical methods for the deformulation of polymeric materials, with emphasis on the non-polymeric components. Each technique is evaluated on its technical and industrial merits. Emphasis is on understanding (principles and characteristics) and industrial applicability. Extensively illustrated throughout with over 200 figures, 400 tables, and 3,000 references.
Frans Hals is one of the most important portrait painters of all time. Like Rembrandt, the famous Dutch Baroque master's striking portraits of the bourgeoisie and social outsiders are distinguished by their extraordinary vividness and accurate depiction. His sketch-like paintings, executed with bold brushstrokes, had a decisive influence on modernist painting. This comprehensive publication coincides with the first major survey exhibition of Hals' oeuvre in more than thirty years. FRANS HALS (1582/84–1666) was born in Antwerp, the son of a cloth merchant. In 1610 he was accepted into the Haarlem Guild of St. Luke. Hals created hundreds of genre paintings, individual, and group portraits and enjoyed great public prestige. Despite his fame during his lifetime, it was not until the nineteenth century that he was enthusiastically rediscovered by the Impressionists and Realists.
Bridging Knowledge and Its Implications for Our Perspectives of the World : Proceedings of the Workshop on Times of Entanglement, Minsheng Art Museum, Shanghai, 21-22 September 2010
Bridging Knowledge and Its Implications for Our Perspectives of the World : Proceedings of the Workshop on Times of Entanglement, Minsheng Art Museum, Shanghai, 21-22 September 2010
The present volume is part of the ?Worldviews, Science and Us? series of proceedings. It contains selected contributions on the subject of bridging knowledge and its implications for our perspectives of the world. This volume also represents the proceedings of the interdisciplinary stream of the international workshop (Part 1) Times of Entanglement, 21?22 September 2010 at the Minsheng Art Museum in Shanghai, People's Republic of China in the context of the Shanghai World Expo 2010 and, related cutting-edge investigations in the quantum paradigm from discussion panels organized by the Leo Apostel Center for Interdisciplinary studies within the framework of the ?Research on the Construction of Integrating Worldviews? research community set up by the Flanders Fund for Scientific Research. Further information about this research community and a full list of the associated international research centers can be found at http: //www.vub.ac.be/CLEA/res/worldviews/.
This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Global Legitimacy Crises addresses the consequences of legitimacy in global governance, in particular asking: when and how do legitimacy crises affect international organizations and their capacity to rule. The book starts with a new conceptualization of legitimacy crisis that looks at public challenges from a variety of actors. Based on this conceptualization, it applies a mixed-methods approach to identify and examine legitimacy crises, starting with a quantitative analysis of mass media data on challenges of a sample of 32 IOs. It shows that some, but not all organizations have experienced legitimacy crises, spread over several decades from 1985 to 2020. Following this, the book presents a qualitative study to further examine legitimacy crises of two selected case studies: the WTO and the UNFCCC. Whereas earlier research assumed that legitimacy crises have negative consequences, the book introduces a theoretical framework that privileges the activation inherent in a legitimacy crisis. It holds that this activation may not only harm an IO, but could also strengthen it, in terms of its material, institutional, and decision-making capacity. The following statistical analysis shows that whether a crisis has predominantly negative or positive effects depends on a variety of factors. These include the specific audience whose challenges define a certain crisis, and several institutional properties of the targeted organization. The ensuing in-depth analysis of the WTO and the UNFCCC further reveals how legitimacy crises and both positive and negative consequences are interlinked, and that effects of crises are sometimes even visible beyond the organizational borders.
Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.
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