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Induction and Learning

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Book cover Uncertainty Modeling for Data Mining

Part of the book series: Advanced Topics in Science and Technology in China ((ATSTC))

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Abstract

Induction is fundamental to the acquisition of human knowledge. Twenty-four centuries ago, Plato raised the point that people have much more knowledge than what appears to be present in the information to which they have been exposed. Chomsky referred to it as Plato’s problem to describe the gap between knowledge and experience[1]. Induction can be regarded as an important property of intelligence. Human beings have the ability of generalizing from already known cases to new unknown cases with which they share similarities or patterns. Actually, people have been seeking patterns in data throughout human history. Hunters seek patterns in animal migration behavior in order to hunt for survival, farmers seek patterns in crop growth in order to feed themselves and their families, businessmen seek patterns from markets to make profit, and politicians seek patterns in voter opinions in order to be elected. A scientist’s job is to make sense of observed evidence (or data) in order to discover the patterns that govern how the physical world works and encapsulate them in theories that can be used for predicting what will happen in the future. Scientists are the first group of people who woke up and dared to argue with the followers of the Almighty on the issues such as the earth is not the center of the universe and human beings, like all other species, have evolved to what they are today. The powerful tool they have been employing, so called science, is based on such a hypothesis-evidence paradigm. With the development of new measuring tools, we can always find more new evidence about the nature and our hypothesis spaces have been updated again and again by those giants like Copernicus, Newton, Maxwell, Darwin and Einstein.

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Authors and Affiliations

  1. Intelligent Computing and Machine Learning Lab, School of ASEE, Beihang University, Beijing, China

    Prof. Zengchang Qin

  2. College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China

    Prof. Yongchuan Tang

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  1. Prof. Zengchang Qin
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© 2014 Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg

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Qin, Z., Tang, Y. (2014). Induction and Learning. In: Uncertainty Modeling for Data Mining. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41251-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-41251-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41250-9

  • Online ISBN: 978-3-642-41251-6

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