الصفحة 6
الصفحة 6
Advances in Distribution Theory, Order Statistics, and Inference
Barry Arnold has made fundamental contributions to many different areas of statistics, including distribution theory, Bayesian inference, multivariate analysis, bounds and orderings, and characterization problems. Organized to honor Arnold’s significant contributions to the field, this volume is an outgrowth of the "International Conference on Distribution Theory, Order Statistics, and Inference," held at the University of Cantabria, Santander, Spain.Several distinguished and active researchers highlight some of the recent developments in statistical distribution theory, order statistics and their properties, as well as inferential methods associated with them. Applications to survival analysis, reliability, quality control, and environmental problems are emphasized.
Advances in artificial intelligence: models, optimization, and machine learning
Contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems.
Advances in Artificial Intelligence ; 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007, Proceedings
This book cover agents, bioinformatics, classification, constraint satisfaction, data mining, knowledge representation and reasoning, learning, natural language, and planning.
Advances in Artificial Intelligence ; 15th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2002 Calgary, Canada, May 27-29, 2002 Proceedings
The AI conference series is the premier event sponsored by the Canadian - ciety for the Computational Studies of Intelligence / Soci´et´e canadienne pour l’´etude d’intelligence par ordinateur. Attendees enjoy our typically Canadian - mosphere –hospitable and stimulating. The Canadian AI conference showcases the excellent research work done by Canadians, their international colleagues, and others choosing to join us each spring. International participation is always high; this year almost 40% of the submitted papers were from non-Canadian - searchers. We accepted 24 papers and 8 poster papers from 52 full-length papers submitted. We also accepted eight of ten abstracts submitted to the Graduate Student Symposium. All of these accepted papers appear in this volume.
Advanced Techniques in Knowledge Discovery and Data Mining
This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis .
Advanced methods for knowledge discovery from complex data
An overview of the field, looking at the issues and challenges involved is followed by coverage of recent trends in data mining, including descriptions of some currently popular tools like genetic algorithms, neural networks and case-based reasoning. This provides the context for the subsequent chapters on methods and applications. Part I is devoted to the foundations of mining different types of complex data like trees, graphs, links and sequences. A knowledge discovery approach based on problem decomposition is also described. Part II presents important applications of advanced mining techniques to data in unconventional and complex domains, such as life sciences, world-wide web, image databases, cyber security and sensor networks. With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, as well as recent working applications this book provides a representative selection of the available methods and their evaluation in real domains. It will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field.
Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues ; 4th International Conference on Intelligent Computing, ICIC 2008 Shanghai, China, September 15-18, 2008 Proceedings
This book - in conjunction with the two volumes CCIS 0015 and LNAI 5226 - constitutes the refereed proceedings of the 4th International Conference on Intelligent Computing, ICIC 2008, held in Shanghai, China, in September 2008.
Advanced Data Mining and Applications ; 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings
This book constitutes the refereed proceedings of the 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, held in Chengdu, China, in October 2008.
Advanced Data Mining and Applications ; 3rd International Conference, ADMA 2007, Harbin, China, August 6-8, 2007 Proceedings
The Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi’an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
A Course in Credibility Theory and its Applications
It covers the subject of Credibility Theory extensively and includes most aspects of this topic from the simplest case to the most general dynamic model. The first four chapters contain plenty of material The book therefore treats explicitly the tasks which the actuary encounters in his daily work such as estimation of loss ratios, claim frequencies and claim sizes. The models are worked out in detail (including the estimation of structural parameters) so that they can immediately be applied in practice. Most exercises are based on real insurance data and real situations from practice and many of them have the characteristics of a case study. The extension to practical problems arising from the general area of finance is often quite straightforward. This book deserves a place on the bookshelf of every actuary and mathematician who works, teaches or does research in the area of insurance and finance.for a first course on Credibility.
