机器学习
候选消除算法旨在找到所有与观察到的训练示例一致且可描述的假设。为了精确定义此算法，我们首先介绍几个基本定义。首先，如果一个假设能够正确分类训练示例，那么我们称该假设与这些示例一致。
定义： 假设 h 与训练示例集 D 一致，当且仅当对于 D 中的每个示例 (x,c(x))，都有 。
注意“一致”（consistent）和“满足”（satisfies）定义之间的区别：
- 无论 x 是目标概念的正例还是负例，当 时，示例 x 被称作满足假设 h。
- 当且仅当 时，示例 x 被称作与假设 h 一致。
定义：版本空间 — 版本空间，记作 VSH,D，是指在给定假设空间 H 和训练示例 D 的情况下，来自 H 中与 D 中的训练示例一致的假设子集。
The Candidate – Elimination algorithm finds all describable hypotheses that are consistent with thobserved training examples. In order to define this algorithm precisely, we begin with a few basic
definitions. First, let us say that a hypothesis is consistent with the training examples if it correctly
classifies these examples.
Definition: A hypothesis h is consistent with a set of training examples D if and only if h(x) = c(x) for
each example (x, c(x)) in D.
Note difference between definitions of consistent and satisfies
An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive
or negative example of the target concept.
An example x is said to consistent with hypothesis h iff h(x) = c(x)
Definition: version space- The version space, denoted V SH, D with respect to hypothesis space H and
training examples D, is the subset of hypothese from H consistent with the training examples in D