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Computing and Combinatorics
The refereed proceedings of the 11th Annual International Computing and Combinatorics Conference, COCOON 2005, held in Kunming, China in August 2005. The 96 revised full papers presented together with abstracts of 3 invited talks were carefully reviewed and selected from 353 submissions. The papers cover most aspects of theoretical computer science and combinatorics related to computing and are organized in topical sections on bioinformatics, networks, string algorithms, scheduling, complexity, steiner trees, graph drawing and layout design, quantum computing, randomized algorithms, geometry, codes, finance, facility location, graph theory, graph algorithms.
Algorithms and Data Structures
The papers in this volume were presented at the 8th Workshop on Algorithms and Data Structures (WADS 2003). The workshop took place July 30–August 1, 2003, at Carleton University in Ottawa, Canada. The workshop alternates with the Scandinavian Workshop on Algorithm Theory (SWAT), continuing the tradition of SWAT and WADS starting with SWAT’88 and WADS’89. In response to the call for papers, 126 papers were submitted. From these submissions, the program committee selected 40 papers for presentation at the workshop. In addition, invited lectures were given by the following distinguished researchers: Gilles Brassard, Dorothea Wagner, Daniel Spielman, and Michael Fellows. Atthisyear’swor...
Combinatorial and Algorithmic Aspects of Networking
TheInternetisamassiveglobalnetworkofover700millionusersanditisadding users at the rate of 300,000 per day. This large, distributed, and everchanging network poses a challenge to researchers: How does one study, model, or und- stand such a decentralized, constantly evolving entity? Research in large-scale networks seeks to address this question, and the unique nature of these networks calls for a range of techniques from a host of disciplines. The workshop Com- natorial and Algorithmic Aspects of Networking and the Internet (CAAN 2004) provided a forum for the exchange of ideas on these topics. The primary goals of the workshop were to bring together a diverse cro- section of researchers in a...
Algorithms - ESA 2006
This book constitutes the refereed proceedings of the 14th Annual European Symposium on Algorithms, ESA 2006, held in Zurich, Switzerland, in the context of the combined conference ALGO 2006. The book presents 70 revised full papers together with abstracts of 3 invited lectures. The papers address all current subjects in algorithmics, reaching from design and analysis issues of algorithms over to real-world applications and engineering of algorithms in various fields.
Internet and Network Economics
This volume contains the papers presented at the International Workshop on Internet and Network Economics held during December 17–20, 2008, in Sha- hai, China, for its fourth edition. WINE 2008 provided a forum for researchers from di?erent disciplines to communicate with each other and exchange their researching ?ndings in this emerging ?eld. WINE 2008hadteninvitedspeakers:FanChungGraham,MatthewJackson, Lawrence Lau, Tom Luo, Eric Maskin, Paul Milgrom, Christos Papadimitriou, Herbert Scarf, Hal Varian and Yinyu Ye. There were 126 submissions. Each submission was reviewed on average by 2. 5 Programme Committee members. The Committee decided to accept 68 papers. The programme also included ...
Algorithms - ESA 2000
This book constitutes the refereed proceedings of the 8th Annual European Symposium on Algorithms, ESA 2000, held in Saarbrücken, Germany in September 2000. The 39 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the book. Among the topics addressed are parallelism, distributed systems, approximation, combinatorial optimization, computational biology, computational geometry, external-memory algorithms, graph algorithms, network algorithms, online algorithms, data compression, symbolic computation, pattern matching, and randomized algorithms.
Automated Machine Learning and Meta-Learning for Multimedia
This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.
Computing and Combinatorics
The refereed proceedings of the 13th Annual International Computing and Combinatorics Conference, COCOON 2007, held in Banff, Canada in July 2007. The 51 revised full papers presented together with abstracts of 3 invited talks were carefully reviewed and selected from 154 submissions. The papers feature original research works in the areas of algorithms, theory of computation, computational complexity, and combinatorics related to computing.
Computational Trust Models and Machine Learning
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.
