定义:考虑对于实例集合X的概念学习算法L.令c为X上定义的任 一概念,并令Dc={}为c的任意训练样例集合.令L(xi ,Dc)表 示经过数据Dc的训练后L赋予实例的分类.L的归纳偏置是最小断 言集合B,它使任意目标概念c和相应的训练样例Dc满足:

3.1 简介 定义：概念学习是指从有关某个布尔函数的输入输出训练 样例中推断出该布尔函数。 3.2 概念学习任务

①事件e的各个选择符被放入PS(partial star)中,将ps中的元素按 照各种标准排序 ②在ps中保留最优的m个选择符. ③对ps中的选择符进行完备性和一致性检查,从ps中取出完备一致的描述放入 SOLUTION表中,若 SOLUTION表的大小大于参数

Determine a star for each seed against the other seed events By appropriately modifying and selecting complexes from stars, construct a disjoint cover of E that optimizes the criterion LEF Is the termination Criterion satisfied?

Determine a star for each seed against the other seed events By appropriately modifying and selecting complexes from stars, construct a disjoint cover of E that optimizes the criterion LEF Is the termination Criterion satisfied?

Goal Concept: A concept definition describing the concept to be learned. (It is assumed that this concept definition fails to satisfy the Operationality Criterion.) Training Example: An example of the goal concept. Domain Theory: A set of rules and facts to be used in explaining how the training example is an example of the goal concept

一. 示例学习的问题描述（见表2.1,表2.2） 二. 决策树学习（ID3算法） 1. 学习效果的衡量标准（示例学习的优化问题） 2. ID3算法：

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