Advances in Large-Margin Classifiers:

Advances in Large-Margin Classifiers

Product Description

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.

About the Author

Alexander J. Smola is a Postdoctoral Fellow at GMD-FIRST, Berlin. Peter L. Bartlett is Senior Fellow, Computer Sciences Laboratory, Australian National University. Bernhard Schölkopf is a Researcher at Microsoft Research Ltd., Cambridge, UK. Dale Schuurmans is Assistant Professor of Computer Science at the University of Waterloo, Ontario, Canada.

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Advances in Metaheuristics for Hard Optimization:

Advances in Metaheuristics for Hard Optimization

Product Description

Many advances have been made recently in metaheuristic methods, from theory to applications. The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general-purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and various metaheuristics.

The book gathers contributions related to the following topics: theoretical developments in metaheuristics; adaptation of discrete metaheuristics to continuous optimization; performance comparisons of metaheuristics; cooperative methods combining different approaches; parallel and distributed metaheuristics for multiobjective optimization; software implementations; and real-world applications.

This book is suitable for practitioners, researchers and graduate students in disciplines such as optimization, heuristics, operations research, and natural computing.

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Secure Data Management in Decentralized Systems (Advances in Information Security):

Secure Data Management in Decentralized Systems (Advances in Information Security)

Product Description

The research scope of database security has expanded greatly, due to the rapid development of the global inter-networked infrastructure. Databases are no longer stand-alone systems that are only accessible to internal users of organizations. Instead, allowing selective access from different security domains has become a must for many business practices. New data services are emerging everyday, which also brings new challenges to protect data security. The internet and the web offer means to collecting and sharing data with unprecedented flexibility and convenience. Secure Data Management in Decentralized Systems identifies and addresses these new challenges. This volume presents a wide range of active areas, closely related to database security by distinguished leaders within this field. Solutions are included. Secure Data Management in Decentralized Systems is designed for a professional audience composed of practitioners and researchers in industry. This book is suitable for graduate-level students in computer science as well.

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Advances in Computer Science and Engineering:

Advances in Computer Science and Engineering

Product Description
This book constitutes the revised selected papers of the 13th International CSI Computer Conference, CSICC 2008 held on Kish Island, Iran, in March 2008. The 84 regular papers presented together with 68 poster presentations were carefully reviewed and selected from a total of 426 submissions. The papers are organized in topical sections on learning/soft computing, algorithm theory, SoC and NoC, wireless/sensor networks, video processing and related topics, processor architecture, AI/robotics/control, medical image processing, p2p/cluster/grid systems, mobile ad hoc networks, web, signal processing/speech processing, misc, security, image processing applications as well as VLSI.

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