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Titlebook: Learning with Nested Generalized Exemplars; Steven L. Salzberg Book 1990 Kluwer Academic Publishers 1990 algorithms.artificial intelligenc

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發(fā)表于 2025-3-21 17:57:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Learning with Nested Generalized Exemplars
編輯Steven L. Salzberg
視頻videohttp://file.papertrans.cn/584/583025/583025.mp4
叢書(shū)名稱(chēng)The Springer International Series in Engineering and Computer Science
圖書(shū)封面Titlebook: Learning with Nested Generalized Exemplars;  Steven L. Salzberg Book 1990 Kluwer Academic Publishers 1990 algorithms.artificial intelligenc
描述Machine Learning is one of the oldest and most intriguing areas of Ar- tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa- tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com- puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in- clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di- agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and a
出版日期Book 1990
關(guān)鍵詞algorithms; artificial intelligence; automatic classification; behavior; classification; cluster analysis
版次1
doihttps://doi.org/10.1007/978-1-4613-1549-0
isbn_softcover978-1-4612-8830-5
isbn_ebook978-1-4613-1549-0Series ISSN 0893-3405
issn_series 0893-3405
copyrightKluwer Academic Publishers 1990
The information of publication is updating

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沙發(fā)
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The NGE learning algorithm,ould use them, rather than at the beginning. Each of the sections which follows, then, is a phase of the program’s operation. For the sake of brevity, descriptions of the data structures and minor subroutines have been omitted.
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0893-3405 t began to conceive the potential for general-purpose symbolic computa- tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com- puter programs that can be said to learn, we are still far from achieving the lofty v
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aim of maintaining a sense of identity security of their citizens”. This redefinition was accomplished by “blurring” national borders, which traditionally represented a clear dividing line between the collective perception of ‘Self’ in relation to the ‘Other’, thus enabling the space for the MS’ co
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Steven L. Salzberg aim of maintaining a sense of identity security of their citizens”. This redefinition was accomplished by “blurring” national borders, which traditionally represented a clear dividing line between the collective perception of ‘Self’ in relation to the ‘Other’, thus enabling the space for the MS’ co
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發(fā)表于 2025-3-23 00:26:10 | 只看該作者
Steven L. Salzbergrking intensively with tools, machinery, materials and other accessories of trades work. As trades workers perform work tasks, their tools, machinery, etc. provide a range of sensory and tactile responses, some of which are crucial to acknowledge towards ensuring the optimal achievement of work task
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Steven L. Salzbergation and identities. She thus highlights questions of the ‘encultured, affective, corporeal and located nature of musical experience’ (2010: 89). Similarly, in 2007 the composer/improvisor George Lewis made the argument for listening as a mode essentially linked to the body and thus deeply tied lis
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