Lifelong Machine Learning

Zhiyuan Chen, Bing Liu

  • 出版商: Morgan & Claypool
  • 出版日期: 2016-11-07
  • 售價: $1,890
  • 貴賓價: 9.5$1,796
  • 語言: 英文
  • 頁數: 146
  • 裝訂: Paperback
  • ISBN: 1627055010
  • ISBN-13: 9781627055017
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.

商品描述(中文翻譯)

終身機器學習(或終身學習)是一種先進的機器學習範式,它持續學習,累積在先前任務中學到的知識,並將其用於幫助未來的學習。在這個過程中,學習者變得越來越有知識和有效地學習能力。這種學習能力是人類智慧的標誌之一。然而,目前主流的機器學習範式是「孤立學習」:給定一個訓練數據集,它在數據集上運行機器學習算法以生成模型。它不試圖保留已學到的知識並在未來的學習中使用。儘管這種孤立學習範式非常成功,但它需要大量的訓練示例,並且只適用於明確且狹窄的任務。相比之下,我們人類可以在很少的示例下有效學習,因為我們在過去累積了很多知識,這使我們能夠以少量的數據或努力進行學習。終身學習旨在實現這種能力。隨著統計機器學習的成熟,現在是努力打破孤立學習傳統並研究終身學習,將機器學習推向新的高度的時候了。智能助手、聊天機器人和與人類和系統在現實環境中互動的物理機器人等應用也需要這種終身學習能力。如果一個系統無法累積已學到的知識並用它來增量學習更多知識,那麼它可能永遠不會真正具有智能。本書作為一本介紹性的文本和終身學習概述。