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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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概括
此摘要是机器生成的。

现代科学仪器产生大数据,需要信息提取. 一个新的混合建模框架将这些原始数据转化为未来科学应用的有意义的见解.

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科学领域:

  • 数据科学数据科学数据科学
  • 科学计算科学计算
  • 信息科学 信息科学 信息科学

背景情况:

  • 现代科学仪器产生大量的数据 (大数据).
  • 有效利用这些大数据需要强大的信息提取方法.
  • 当前的数据处理方法可能无法充分解决科学大数据的规模和复杂性.

研究的目的:

  • 为大数据信息提取提供混合建模框架.
  • 将原始,无意义的数据转化为可操作,有意义的信息.
  • 为未来科学技术中的大数据处理奠定基础.

主要方法:

  • 开发一个混合建模框架.
  • 框架应用的说明. 框架应用的说明.
  • 专注于大数据的信息提取技术.

主要成果:

  • 证明成功地将毫无意义的数据转化为有意义的信息.
  • 验证混合模型在数据处理中的有效性.
  • 建立用于大数据分析的实用方法.

结论:

  • 混合建模框架为科学中的大数据挑战提供了可行的解决方案.
  • 有效的信息提取对于利用大数据至关重要.
  • 该框架支持大数据管理的理论,实践和民主方法.