| 書目名稱 | Multistrategy Learning | | 副標題 | A Special Issue of M | | 編輯 | Ryszard S. Michalski | | 視頻video | http://file.papertrans.cn/642/641239/641239.mp4 | | 叢書名稱 | The Springer International Series in Engineering and Computer Science | | 圖書封面 |  | | 描述 | Most machine learning research has been concerned with thedevelopment of systems that implememnt one type of inference within asingle representational paradigm. Such systems, which can be called.monostrategy. learning systems, include those for empiricalinduction of decision trees or rules, explanation-basedgeneralization, neural net learning from examples, geneticalgorithm-based learning, and others. Monostrategy learning systemscan be very effective and useful if learning problems to which theyare applied are sufficiently narrowly defined..Many real-world applications, however, pose learning problems that gobeyond the capability of monostrategy learning methods. In view ofthis, recent years have witnessed a growing interest in developing.m.ultistrategy systems., which integrate two or moreinference types and/or paradigms within one learning system. Suchmultistrategy systems take advantage of the complementarity ofdifferent inference types or representational mechanisms. Therefore,they have a potential to be more versatile and more powerful thanmonostrategy systems. On the other hand, due to their greatercomplexity, their development is significantly more difficult andrepresents a | | 出版日期 | Book 1993 | | 關(guān)鍵詞 | algorithms; complexity; decision tree; genetic algorithms; knowledge; learning; machine learning; modeling; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-3202-6 | | isbn_softcover | 978-1-4613-6405-4 | | isbn_ebook | 978-1-4615-3202-6Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 1993 |
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