Lazy vs. Eager Learning

  • Lazy vs. eager learning
    • Lazy learning (e.g., instance-based learning): Simply stores training data (or only minor processing) and waits until it is given a test tuple
    • Eager learning (the discussed methods so far): Given a set of training tuples, constructs a classification model before receiving new (e.g., test) data to classify

惰性学习(例如，基于实例的学习):简单地存储训练数据(或只进行少量处理)，直到它得到一个测试元组
热切学习(目前讨论的方法):给定一组训练元组，在接收新的(如测试)数据进行分类之前，构造一个分类模型

  • Lazy: less time in training but more time in predicting 训练时间少，但预测花费时间长。
  • Accuracy
    • Lazy method effectively uses a richer hypothesis space since it uses many local linear functions to form an implicit global approximation to the target function
    • Eager: must commit to a single hypothesis that covers the entire instance space

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Lazy Learner: Instance based method 基于实例的方法

  • Instance-based learning:
    • Store training examples and delay the processing (“lazy evaluation”) until a new instance must be classified
  • Typical approaches
    • k-nearest neighbor approach (KNN)
      • Instances represented as points in a Euclidean space. 在欧几里得空间中表达实例点。
    • Locally weighted regression 局部加权回归
      • Constructs local approximation 构造局部逼近
    • Case-based reasoning
      • Uses symbolic representations and knowledge-based inference 基于符号和知识的推理