Reduced-Error Pruning Split data into training and validation set Do until further pruning is harmful: 1. Evaluate impact on validation set of pruning each possible node (plus those below it) 2. Greedily remove the one that most improves validation set accuracy produces smallest version of most accurate subtree · What if data is limited?

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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