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Advances in Web Intelligence and Data Mining
This book presents state-of-the-art developments in the area of computationally intelligent methods applied to various aspects and ways of Web exploration and Web mining. Some novel data mining algorithms that can lead to more effective and intelligent Web-based systems are also described. Scientists, engineers, and research students can expect to find many inspiring ideas in this volume.
Pattern Recognition with Support Vector Machines
This book constitutes the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, held in Niagara Falls, Canada in August 2002.The 16 revised full papers and 14 poster papers presented together with two invited contributions were carefully reviewed and selected from 57 full paper submissions. The papers presented span the whole range of topics in pattern recognition with support vector machines from computational theories to implementations and applications.
Data Analytics Using Open-Source Tools
This book is about using open-source tools in data analytics. The book covers several subjects, including descriptive and predictive modeling, gradient boosting, cluster modeling, logistic regression, and artificial neural networks, among other topics.
Pattern Classification Using Ensemble Methods
Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
Introduction to Semi-Supervised Learning
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mi...
AI 2002: Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 15th Australian Joint Conference on Artificial Intelligence, AI 2002, held in Canberra, Australia in December 2002. The 62 revised full papers and 12 posters presented were carefully reviewed and selected from 117 submissions. The papers are organized in topical sections on natural language and information retrieval, knowledge representation and reasoning, deduction, learning theory, agents, intelligent systems. Bayesian reasoning and classification, evolutionary algorithms, neural networks, reinforcement learning, constraints and scheduling, neural network applications, satisfiability reasoning, machine learning applications, fuzzy reasoning, and case-based reasoning.
Product Development in the Socio-sphere
This book provides a broad overview of a number of game-changing paradigms that are anticipated to reshape 21st century product development. Topics including cloud computing-based design, cloud manufacturing, crowd-sourcing and mass collaboration, open source and social product development will be discussed in the context of advanced distributed and collaborative product creation. The purpose of the book is threefold: (1) to provide decision makers in industry with a solid base for strategic design and manufacturing-related process re-organization; (2) to provide researchers and scientist with the state-of-the-art from an academic perspective as well as a research agenda aimed at advancing the theoretical foundations of the field and (3) to serve as supplementary reading in design and manufacturing-related courses at universities and technical colleges.
Advances in Large Margin Classifiers
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. 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.
Learning Theory
This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004. The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.
Variation in Time and Space
Variation in Time and Space: Observing the World through Corpora is a collection of articles that address the theme of linguistic variation in English in its broadest sense. Current research in English language presented in the book explores a fascinating number of topics, whose unifying element is the corpus linguistic methodology. Part I of this volume, Meaning in Time and Space, introduces the two dimensions of variation – time and space – relating them to the negotiation of meaning in discourse and questions of intertextuality. Part II, Variation in Time, approaches the English language from a diachronic point of view; the time periods covered vary considerably, ranging from 16th century up to present-day; so do the genres explored. Part III, Variation in Space, focuses on global varieties of English and includes a contrastive point of view. The range of topics is again broad – from specific lexico-grammatical structures to the variation in academic English, combining the regional and genre dimensions of variation. This is a timely volume that shows the breadth and depth in current corpus-based research of English.
