Learning Kernel Classifiers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning Kernel Classifiers write by Ralf Herbrich. This book was released on 2022-11-01. Learning Kernel Classifiers available in PDF, EPUB and Kindle. 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.
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances in Large Margin Classifiers write by Alexander J. Smola. This book was released on 2000. Advances in Large Margin Classifiers available in PDF, EPUB and Kindle. 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.
Hybrid Classifiers
Hybrid Classifiers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hybrid Classifiers write by Michal Wozniak. This book was released on 2013-09-16. Hybrid Classifiers available in PDF, EPUB and Kindle. 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.
Classifiers
Classifiers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Classifiers write by Alexandra Y. Aikhenvald. This book was released on 2000-03-30. Classifiers available in PDF, EPUB and Kindle. 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.
Numeral Classifiers in Chinese
Numeral Classifiers in Chinese - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Numeral Classifiers in Chinese write by XuPing Li. This book was released on 2013-08-29. Numeral Classifiers in Chinese available in PDF, EPUB and Kindle. 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.
