• Machine learning preface


    Machine learning Preface

    Definition

    • T: Task
    • E: Experience
    • P: Performance
    • Sequence: T -> E -> P

    Supervised learning

    Definition

    • Give the right answer to each example of the data set(called training data).

    Type

    • Regression: get the continuous values
    • Classification: get the discrete values like 0, 1, 2, 3 and so on

    application scenarios

    • Regression: predict the price of the house based on the square, location of the house

      • house price
    • Classification:

      • Tumor prediction
      • Spam filter

    Unsupervised learning

    Type

    • Cluster algorithm

    application scenarios

    • Google news: get lots of related news in the Internet and put them in one set of URL.
    • Social network: find the common friends.
    • Market segmentation: We all know the data, but we don't know the what kinds of market segmentation, so let unsupervised learning to deal with it.
    • Extract human voice from records: you know, there are some noise in these records, we need to get the human voice, so we let cluster algorithm to deal with.

    Others

    Recommender system

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  • 原文地址:https://www.cnblogs.com/megachen/p/9903276.html
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