| 書目名稱 | Learning to Learn | | 編輯 | Sebastian Thrun,Lorien Pratt | | 視頻video | http://file.papertrans.cn/583/582999/582999.mp4 | | 圖書封面 |  | | 描述 | Over the past three decades or so, research on machine learningand data mining has led to a wide variety of algorithms that learngeneral functions from experience. As machine learning is maturing, ithas begun to make the successful transition from academic research tovarious practical applications. Generic techniques such as decisiontrees and artificial neural networks, for example, are now being usedin various commercial and industrial applications. .Learning to Learn is an exciting new research direction within machinelearning. Similar to traditional machine-learning algorithms, themethods described in .Learning to Learn. induce general functionsfrom experience. However, the book investigates algorithms that canchange the way they generalize, i.e., practice the task of learningitself, and improve on it. .To illustrate the utility of learning to learn, it is worthwhilecomparing machine learning with human learning. Humans encounter acontinual stream of learning tasks. They do not just learn concepts ormotor skills, they also learn .bias., i.e., they learn how togeneralize. As a result, humans are often able to generalize correctlyfrom extremely few examples - often just a single e | | 出版日期 | Book 1998 | | 關(guān)鍵詞 | algorithms; artificial neural network; cognition; control; data mining; decision tree; knowledge; learning; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-5529-2 | | isbn_softcover | 978-1-4613-7527-2 | | isbn_ebook | 978-1-4615-5529-2 | | copyright | Springer Science+Business Media New York 1998 |
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