ExampleⅤ ersion Space S:1(<Sunny, Warm,2, Strong, 2, 2>) <Sunny, ? 2, Strong, 2, ? <Sunny, Warm,2,2,2,?><2, Warm, 2, Strong, 2,?> G:{≤Smy,2,?,??,?>,<2,Wa?,??,?>}
How Should these be classified? S:(<Sunny, Warm, ? Strong, 2, 2>3 Smy,?,?,Stog,?,?<Smmy,Wa,2,2,?,2<?,Wam,?,Smog,?,?> G:{<Say,?,?,?,?,?>,<?,Wam?,?,??>} Sunny ormal Strong Cool Change) ( Rainy Cool Normal Light Warm Same) (Sunny Warm Normal Light Warm Same)
Instance, Hypotheses, and More General-Than Instances X Hypotheses H General x1<Summy, Warm, High, Strong, Cool, Same> h,=<Summmy, 2, 2, Strong, 2,2> Summy, Warm, High, Light, Warm, Same h=<m.?.?.?.?,> h=<Sum. ??? Cool
Version Spaces A hypothesis h is consistent with a set of training examples D of target concept c if and only if h()=c(a) for each training example 〈x,c(x)inD Consistent(h,D)≡(x,c(x)∈D)h(x)=c(x) The version space, VSH.D, with respect to hypothesis space H and training examples D is the subset of hypotheses from H consistent with all training examples in D SnD≡{h∈ H Consistent(h,D)}
第四章示例学习的实用化 41定量属性的定性化 等宽离散法 2.决策树连续属性值处理(二分离散法) 1)切点法 T 类熵( class entropy)
第四章 示例学习的实用化 4.1 定量属性的定性化 1. 等宽离散法 L 2. 决策树连续属性值处理(二分离散法) 1) 切点法: T 类熵(class entropy)