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.

Almost all languages have some ways of categorizing nouns. Languages of South-East Asia have classifiers used with numerals, while most Indo-European languages have two or three genders. They can have a similar meaning and one can develop from the other. This book provides a comprehensive and original analysis of noun categorization devices all over the world. It will interest typologists, those working in the fields of morphosyntactic variation and lexical semantics, as well as anthropologists and all other scholars interested in the mechanisms of human cognition.

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.

This book studies the syntax and semantics of numeral classifiers in Mandarin and other Chinese languages. It explores how Chinese classifiers are semantically interpreted in syntactic contexts and how semantic functions of classifiers are realized at the syntactic level. The book is a contribution to formal Chinese linguistics, and to the understanding of grammatical properties of nominal phrases in Chinese and East Asian languages.

This volume offers a comprehensive account of the typology of noun classification across the world's languages. Following a detailed introduction to noun categorization, the chapters in the volume provide in-depth studies of genders and classifiers of different types in a range of South American and Asian languages and language families.

The book is about the numeral classifier system and the acquisition of Japanese classifiers by Japanese children. It consists of two parts. First, it provides a general typological characterization of numeral classifier phrases and discusses problems in determining what constitutes the nature of classifiers. It also discusses the semantic properties of numeral classifiers based on an analysis of four languages from four different language families. Second, it examines the acquisitions of Japanese numeral classifiers by Japanese preschool children, ages 3 to 6, with a primary emphasis on the development of comprehension. The importance of the study is that it reveals that young children have a much greater sensitivity to the conceptual underpinnings of the numeral classifier system than was previously considered to be the case. The research results also provide a converging source of evidence that young children often come to initially grasp the structure of the world in ways that are better understood in cognitive than perceptual terms. The implications will contribute to not only the area of language acquisition but also categorization and conceptual development.

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes: Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.