Classifier Structures in Mandarin Chinese, Niina Ning Zhang
Автор: Niina Ning Zhang Название: Classifier Structures in Mandarin Chinese ISBN: 3110488051 ISBN-13(EAN): 9783110488050 Издательство: Walter de Gruyter Рейтинг: Цена: 24730.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This monograph addresses fundamental syntactic issues of classifier constructions, based on a thorough study of a typical classifier language, Mandarin Chinese. It shows that the contrast between count and mass is not binary. Instead, there are two independently attested features: Numerability, the ability of a noun to combine with a numeral directly, and Delimitability, the ability of a noun to be modified by a delimitive modifier, such as size, shape, or boundary modifier. Although all nouns in Chinese are non-count nouns, there is still a mass/non-mass contrast, with mass nouns selected by individuating classifiers and non-mass nouns selected by individual classifiers. Some languages have the counterparts of Chinese individuating classifiers only, some languages have the counterparts of Chinese individual classifiers only, and some other languages have no counterpart of either individual or individuating classifiers of Chinese. The book also reports that unit plurality can be expressed by reduplicative classifiers in the language. Moreover, for the constituency of a numeral expression, an individual, individuating, or kind classifier combines with the noun first and then the numeral is integrated; but a partitive or collective classifier, like a measure word, combines with the numeral first, before the noun is integrated into the whole nominal structure. Furthermore, the book identifies the syntactic positions of various uses of classifiers in the language. A classifier is at a functional head position that has a dependency with a numeral, or a position that has a dependency with a generic or existential quantifier, or a position that represents the singular-plural contrast, or a position that licenses a delimitive modifier when the classifier occurs in a compound.
Автор: Niina Ning Zhang Название: Classifier Structures in Mandarin Chinese ISBN: 3110303744 ISBN-13(EAN): 9783110303742 Издательство: Walter de Gruyter Рейтинг: Цена: 161100.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This monograph addresses fundamental syntactic issues of classifier constructions, based on a thorough study of a typical classifier language, Mandarin Chinese. It shows that the contrast between count and mass is not binary. Instead, there are two independently attested features: Numerability, the ability of a noun to combine with a numeral directly, and Delimitability, the ability of a noun to be modified by a delimitive modifier, such as size, shape, or boundary modifier. Although all nouns in Chinese are non-count nouns, there is still a mass/non-mass contrast, with mass nouns selected by individuating classifiers and non-mass nouns selected by individual classifiers. Some languages have the counterparts of Chinese individuating classifiers only, some languages have the counterparts of Chinese individual classifiers only, and some other languages have no counterpart of either individual or individuating classifiers of Chinese. The book also reports that unit plurality can be expressed by reduplicative classifiers in the language. Moreover, for the constituency of a numeral expression, an individual, individuating, or kind classifier combines with the noun first and then the numeral is integrated; but a partitive or collective classifier, like a measure word, combines with the numeral first, before the noun is integrated into the whole nominal structure. Furthermore, the book identifies the syntactic positions of various uses of classifiers in the language. A classifier is at a functional head position that has a dependency with a numeral, or a position that has a dependency with a generic or existential quantifier, or a position that represents the singular-plural contrast, or a position that licenses a delimitive modifier when the classifier occurs in a compound.
Автор: Kovacs, Tim Название: Strength or accuracy: credit assignment in learning classifier systems ISBN: 1447110587 ISBN-13(EAN): 9781447110583 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Classifier systems are an intriguing approach to a broad range of machine learning problems, based on automated generation and evaluation of condi tion/action rules.
Автор: Josef Kittler; Fabio Roli Название: Multiple Classifier Systems ISBN: 3540677046 ISBN-13(EAN): 9783540677048 Издательство: Springer Рейтинг: Цена: 88500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This text constitutes the refereed proceedings of the First International Workshop on Multiple Classifier Systems, MCS 2000, held in Cagliari, Italy in June 2000. The 33 revised full papers presented together with five invited papers were carefully reviewed and selected for inclusion in the book.
Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils Название: Learning Classifier Systems ISBN: 3540677291 ISBN-13(EAN): 9783540677291 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This text provides a survey of the current state-of-the-art of LCS and highlights some of the most promising research directions. The first part presents various views on what learning classifier systems are. The second part is devoted to advanced topics of current interest.
Автор: Pier Luca Lanzi; Wolfgang Stolzmann; Stewart W. Wi Название: Learning Classifier Systems ISBN: 3540205446 ISBN-13(EAN): 9783540205449 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII.
Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils Название: Advances in Learning Classifier Systems ISBN: 3540437932 ISBN-13(EAN): 9783540437932 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: These are the refereed post-proceedings of the 4th International Workshop on Learning Classifier Systems, IWLCS 2001. The first part is devoted to theoretical issues, and the second to applications in various fields such as data mining, stock trading, and power distribution networks.
Автор: Ryan J. Urbanowicz; Will N. Browne Название: Introduction to Learning Classifier Systems ISBN: 3662550067 ISBN-13(EAN): 9783662550069 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is an accessible introduction to Learning Classifier Systems (LCS) for undergraduate and postgraduate students, data analysts, and machine learning practitioners.
Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils Название: Advances in Learning Classifier Systems ISBN: 3540424377 ISBN-13(EAN): 9783540424376 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: These are the refereed post-proceedings of the Third International Workshop on Learning Classifier Systems, IWLCS 2000. The papers are organized in topical sections on theory, applications, and advanced architectures.
Автор: Josef Kittler; Fabio Roli Название: Multiple Classifier Systems ISBN: 3540422846 ISBN-13(EAN): 9783540422846 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Jan Drugowitsch Название: Design and Analysis of Learning Classifier Systems ISBN: 3642098614 ISBN-13(EAN): 9783642098611 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
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