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The Classifier
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Download or read book The Classifier written by Wessel Ebersohn and published by Penguin Random House South Africa. This book was released on 2011-06-27 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: What happens to Chris and Ruthie comes naturally to teenagers: they fall in love, obsessively. But it isn’t natural that their love can only survive in secrecy, being against the wishes, even beyond the imagination, of their parents. And above all being illegal. At home Chris half loves, half fears his taciturn father, who never speaks of his important work for the Government. As Chris’s world opens up he learns about his father’s job as head of the province’s Race Classification Office, whose every decision can make or break somebody’s life in the 1970s South Africa. In this moving rites-of-passage story set in extraordinary circumstances, a coloured girl and white boy head for devastating consequences as their vulnerable lives hurtle down a collision course with the pitiless laws of society and the implacable resolve of his father.
Book Synopsis Learning Classifier Systems by : Pier Luca Lanzi
Download or read book Learning Classifier Systems written by Pier Luca Lanzi and published by Springer. This book was released on 2003-11-24 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning Classifier Systems by : Jaume Bacardit
Download or read book Learning Classifier Systems written by Jaume Bacardit and published by Springer. This book was released on 2008-10-17 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.
Book Synopsis Design and Analysis of Learning Classifier Systems by : Jan Drugowitsch
Download or read book Design and Analysis of Learning Classifier Systems written by Jan Drugowitsch and published by Springer. This book was released on 2008-06-17 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is probably best summarized as providing a principled foundation for Learning Classi?er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de?nition – derived from machine learning – of “a good set of cl- si?ers”, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi?ers using that de?nition as a ?tness criterion, seeing ifthe setprovidesa goodsolutionto twodi?erent function approximation problems. It appears to, meaning that in some sense his de?nition of “good set of classi?ers” (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi?ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.
Book Synopsis Multiple Classifier Systems by : Terry Windeatt
Download or read book Multiple Classifier Systems written by Terry Windeatt and published by Springer. This book was released on 2003-08-03 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Performance of a Screw-type, Classifier-cyclone Combination by : P. Stanley Jacobsen
Download or read book Performance of a Screw-type, Classifier-cyclone Combination written by P. Stanley Jacobsen and published by . This book was released on 1962 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Learning Classifier Systems by : Pier L. Lanzi
Download or read book Advances in Learning Classifier Systems written by Pier L. Lanzi and published by Springer. This book was released on 2003-07-31 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.
Book Synopsis Parallelism and Programming in Classifier Systems by : Stephanie Forrest
Download or read book Parallelism and Programming in Classifier Systems written by Stephanie Forrest and published by Elsevier. This book was released on 2014-06-28 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Building Cognitive Applications with IBM Watson Services: Volume 4 Natural Language Classifier by : Marcelo Mota Manhaes
Download or read book Building Cognitive Applications with IBM Watson Services: Volume 4 Natural Language Classifier written by Marcelo Mota Manhaes and published by IBM Redbooks. This book was released on 2017-05-25 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® WatsonTM cognitive computing services. The series includes an overview of specific IBM Watson® services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: Volume 1 Getting Started, SG24-8387 Volume 2 Conversation, SG24-8394 Volume 3 Visual Recognition, SG24-8393 Volume 4 Natural Language Classifier, SG24-8391 Volume 5 Language Translator, SG24-8392 Volume 6 Speech to Text and Text to Speech, SG24-8388 Volume 7 Natural Language Understanding, SG24-8398 Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 4, introduces the Watson Natural Language Classifier service. This service applies cognitive computing techniques to return best matching predefined classes for short text inputs such as a sentence or phrase. The book describes concepts that you need to understand to create, use and train the classifier. This book describes how to prepare training data, and create and train the classifier to connect the classes to example texts so the service can apply the classes to new inputs. It provides examples of applications that demonstrate how to use the Watson Natural Language Classifier service in practical use cases. You can develop and deploy the sample applications by following along in a step-by-step approach and using provided code snippets. Alternatively, you can download an existing Git project to more quickly deploy the application.
Book Synopsis Learning Classifier Systems in Data Mining by : Larry Bull
Download or read book Learning Classifier Systems in Data Mining written by Larry Bull and published by Springer Science & Business Media. This book was released on 2008-05-29 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability of Learning Classifier Systems (LCS) to solve complex real-world problems is becoming clear. This book brings together work by a number of individuals who demonstrate the good performance of LCS in a variety of domains.
Book Synopsis Numeral Classifiers and Classifier Languages by : Chungmin Lee
Download or read book Numeral Classifiers and Classifier Languages written by Chungmin Lee and published by Taylor & Francis. This book was released on 2021-02-17 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing mainly on classifiers, Numeral Classifiers and Classifier Languages offers a deep investigation of three major classifier languages: Chinese, Japanese, and Korean. This book provides detailed discussions well supported by empirical evidence and corpus analyses. Theoretical hypotheses regarding differences and commonalities between numeral classifier languages and other mainly article languages are tested to seek universals or typological characteristics. The essays collected here from leading scholars in different fields promise to be greatly significant in the field of linguistics for several reasons. First, it targets three representative classifier languages in Asia. It also provides critical clues and suggests solutions to syntactic, semantic, psychological, and philosophical issues about classifier constructions. Finally, it addresses ensuing debates that may arise in the field of linguistics in general and neighboring inter-disciplinary areas. This book should be of great interest to advanced students and scholars of East Asian languages.
Book Synopsis Learning Classifier Systems by : Tim Kovacs
Download or read book Learning Classifier Systems written by Tim Kovacs and published by Springer. This book was released on 2007-06-11 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005. Topics in the 22 revised full papers range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.
Book Synopsis Multiple Classifier Systems by : Fabio Roli
Download or read book Multiple Classifier Systems written by Fabio Roli and published by Springer Science & Business Media. This book was released on 2004-06 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Multiple Classifier Systems, MCS 2004, held in Cagliari, Italy in June 2004. The 35 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on bagging and boosting, combination methods, design methods, performance analysis, and applications.
Book Synopsis Anticipatory Learning Classifier Systems by : Martin V. Butz
Download or read book Anticipatory Learning Classifier Systems written by Martin V. Butz and published by Springer Science & Business Media. This book was released on 2002-01-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Multiple Classifier Systems by : Josef Kittler
Download or read book Multiple Classifier Systems written by Josef Kittler and published by Springer. This book was released on 2003-05-15 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.
Book Synopsis Multiple Classifier Systems by : Jón Atli Benediktsson
Download or read book Multiple Classifier Systems written by Jón Atli Benediktsson and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, held in Reykjavik, Iceland, in June 2009. The 52 revised full papers presented together with 2 invited papers were carefully reviewed and selected from more than 70 initial submissions. The papers are organized in topical sections on ECOC boosting and bagging, MCS in remote sensing, unbalanced data and decision templates, stacked generalization and active learning, concept drift, missing values and random forest, SVM ensembles, fusion of graphics, concepts and categorical data, clustering, and finally theory, methods and applications of MCS.
Book Synopsis Fuzzy Classifier Design by : Ludmila I. Kuncheva
Download or read book Fuzzy Classifier Design written by Ludmila I. Kuncheva and published by Physica. This book was released on 2012-11-08 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever since have been a center of many discussions, fervently admired and condemned. Both proponents and opponents consider the argu ments pointless because none of them would step back from their territory. And stiH, discussions burst out from a single sparkle like a conference pa per or a message on some fuzzy-mail newsgroup. Here is an excerpt from an e-mail messagepostedin1993tofuzzy-mail@vexpert. dbai. twvien. ac. at. by somebody who signed "Dave". , . . . Why then the "logic" in "fuzzy logic"? I don't think anyone has successfully used fuzzy sets for logical inference, nor do I think anyone wiH. In my admittedly neophyte opinion, "fuzzy logic" is a misnomer, an oxymoron. (1 would be delighted to be proven wrong on that. ) . . . I carne to the fuzzy literature with an open mind (and open wal let), high hopes and keen interest. I am very much disiHusioned with "fuzzy" per se, but I did happen across some extremely interesting things along the way. " Dave, thanks for the nice quote! Enthusiastic on the surface, are not many of us suspicious deep down? In some books and journals the word fuzzy is religiously avoided: fuzzy set theory is viewed as a second-hand cheap trick whose aim is nothing else but to devalue good classical theories and open up the way to lazy ignorants and newcomers.
