Classifiers
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Multiple Classifier Systems
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Author : Michal Haindl
language : en
Publisher: Springer
Release Date : 2007-06-21
Multiple Classifier Systems written by Michal Haindl and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-21 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. It covers kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.
A Guide To Gender And Classifiers
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Author : Alexandra Y. Aikhenvald
language : en
Publisher: Oxford University Press
Release Date : 2025-02-12
A Guide To Gender And Classifiers written by Alexandra Y. Aikhenvald and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-12 with Language Arts & Disciplines categories.
This book explores the range of noun categorization devices found in the languages of the world, from the extensive systems of numeral classifiers in Southeast Asia to the highly grammaticalized gender agreement classes in Indo-European languages. Almost all languages use some type of noun categorization device in their grammar, with the most widespread being linguistic gender, whereby nouns are classified based on core semantic properties such as sex, animacy, humanness, or shape and size. Numeral classifiers are also common, and classify a noun in terms of its inherent nature, animacy, shape, and form, accompanied by a numeral or a quantifier. Other types of noun categorization devices include noun classifiers, possessive classifiers, verbal classifiers, and a number of rarer types such as locative and deictic classifiers. In this volume, Alexandra Aikhenvald investigates all facets of these nominal categorization systems, from their form and distribution to their origins, development, and loss. Noun categorization devices provide unique insights into how people categorize the world through the language: in one language, a human might be classified in terms of orientation, hence as 'vertical', in another as male or female, and in another as simply 'animate' or even 'rational'. They also change as society changes, reflecting the ways in which language and social environment are integrated into a single whole.
Numeral Classifier Systems
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Author : Pamela Downing
language : en
Publisher: John Benjamins Publishing
Release Date : 1996-01-01
Numeral Classifier Systems written by Pamela Downing and has been published by John Benjamins Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-01-01 with Language Arts & Disciplines categories.
Numeral Classifier Systems considers the functional significance of the Japanese numeral system, its conclusions based on a corpus of 500 uses of classifier constructions drawn from oral and written Japanese texts. Interestingly, although the Japanese system appears to conform at least superficially to universalistic predictions about its semantic structure, this study reports that in actual usage, the semantic role of classifiers is slight only very rarely do they carry any lexical information unavailable from the context or the noun with which the classifier occurs. It does appear, however, that the system has an important role to play in providing pronoun-like anaphoric elements and in marking pragmatic distinctions such as the individuatedness of referents and the newness of numerical information. For these reasons, the classifier system is deeply involved in a number of subsystems of Japanese grammar, and the demise of the system (sometimes rumored to be impending) would have substantial implications for the structure of the language as a whole.
Multiple Classifier Systems
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Author : Terry Windeatt
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-05-27
Multiple Classifier Systems written by Terry Windeatt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-27 with Business & Economics categories.
This book constitutes the refereed proceedings of the 4th International Workshop on Multiple Classifier Systems, MCS 2003, held in Guildford, UK in June 2003. The 40 revised full papers presented with one invited paper were carefully reviewed and selected for presentation. The papers are organized in topical sections on boosting, combination rules, multi-class methods, fusion schemes and architectures, neural network ensembles, ensemble strategies, and applications
Parallelism And Programming In Classifier Systems
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Author : Stephanie Forrest
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Parallelism And Programming In Classifier Systems written by Stephanie Forrest and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Parallelism and Programming in Classifier Systems deals with the computational properties of the underlying parallel machine, including computational completeness, programming and representation techniques, and efficiency of algorithms. In particular, efficient classifier system implementations of symbolic data structures and reasoning procedures are presented and analyzed in detail. The book shows how classifier systems can be used to implement a set of useful operations for the classification of knowledge in semantic networks. A subset of the KL-ONE language was chosen to demonstrate these operations. Specifically, the system performs the following tasks: (1) given the KL-ONE description of a particular semantic network, the system produces a set of production rules (classifiers) that represent the network; and (2) given the description of a new term, the system determines the proper location of the new term in the existing network. These two parts of the system are described in detail. The implementation reveals certain computational properties of classifier systems, including completeness, operations that are particularly natural and efficient, and those that are quite awkward. The book shows how high-level symbolic structures can be built up from classifier systems, and it demonstrates that the parallelism of classifier systems can be exploited to implement them efficiently. This is significant since classifier systems must construct large sophisticated models and reason about them if they are to be truly ""intelligent."" Parallel organizations are of interest to many areas of computer science, such as hardware specification, programming language design, configuration of networks of separate machines, and artificial intelligence This book concentrates on a particular type of parallel organization and a particular problem in the area of AI, but the principles that are elucidated are applicable in the wider setting of computer science.
Learning Classifier Systems
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Author : Pier Luca Lanzi
language : en
Publisher: Springer
Release Date : 2003-11-24
Learning Classifier Systems written by Pier Luca Lanzi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-24 with Computers categories.
The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7–8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.
Perspectives On Classifier Constructions In Sign Languages
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Author : Karen Emmorey
language : en
Publisher: Psychology Press
Release Date : 2003-04-02
Perspectives On Classifier Constructions In Sign Languages written by Karen Emmorey and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-04-02 with Psychology categories.
Classifier constructions are universal to sign languages and exhibit unique properties that arise from the nature of the visual-gestural modality. The major goals are to bring to light critical issues related to the study of classifier constructions and to present state-of-the-art linguistic and psycholinguistic analyses of these constructions. It is hoped that by doing so, more researchers will be inspired to investigate the nature of classifier constructions across signed languages and further explore the unique aspects of these forms. The papers in this volume discuss the following issues: *how sign language classifiers differ from spoken languages; *cross-linguistic variation in sign language classifier systems; *the role of gesture; *the nature of morpho-syntactic and phonological constraints on classifier constructions; *the grammaticization process for these forms; and *the acquisition of classifier forms. Divided into four parts, groups of papers focus on a particular set of issues, and commentary papers end each section.
Multiple Classifier Systems
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Author : Fabio Roli
language : en
Publisher: Springer
Release Date : 2003-08-02
Multiple Classifier Systems written by Fabio Roli and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-02 with Computers categories.
This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications.
Multiple Classifier Systems
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Author : Jón Atli Benediktsson
language : en
Publisher: Springer
Release Date : 2009-06-10
Multiple Classifier Systems written by Jón Atli Benediktsson and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-10 with Computers categories.
These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2009, held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-established series of meetings providing an inter- tional forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks,pattern recognition,machine learning and stat- tics) concerned with this research topic. From more than 70 submissions, the Program Committee selected 54 papers to create an interesting scienti?c program. The special focus of MCS 2009 was on the application of multiple classi?er systems in remote sensing. This part- ular application uses multiple classi?ers for raw data fusion, feature level fusion and decision level fusion. In addition to the excellent regular submission in the technical program, outstanding contributions were made by invited speakers Melba Crawford from Purdue University and Zhi-Hua Zhou of Nanjing Univ- sity. Papers of these talks are included in these workshop proceedings. With the workshop’sapplicationfocusbeingonremotesensing,Prof.Crawford’sexpertise in the use of multiple classi?cation systems in this context made the discussions on this topic at MCS 2009 particularly fruitful.
Anticipatory Learning Classifier Systems
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Author : Martin V. Butz
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Anticipatory Learning Classifier Systems written by Martin V. Butz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.