adds current best learning

Author: SnShine

md/Current-Best-Learning.md

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+# CURRENT-BEST-LEARNING
+
+## AIMA3e
+__function__ Current-Best-Learning(_examples_, _h_) __returns__ a hypothesis or fail  
+ __if__ _examples_ is empty __then__  
+   __return__ _h_  
+ _e_ ← First(_examples_)  
+ __if__ _e_ is consistent with _h_ __then__  
+   __return__ Current-Best-Learning(Rest(_examples_), _h_)  
+ __else if__ _e_ is a false positive for _h_ __then__  
+   __for each__ _h'_ __in__ specializations of _h_ consistent with _examples_ seen so far __do__  
+     _h''_ ← Current-Best-Learning(Rest(_examples_), _h'_)  
+     __if__ _h''_ ≠ _fail_ __then return__ _h''_  
+ __else if__ _e_ is a false negative for _h_ __then__  
+   __for each__ _h'_ __in__ generalizations of _h_ consistent with _examples_ seen so far __do__  
+     _h''_ ← Current-Best-Learning(Rest(_examples_), _h'_)  
+     __if__ _h''_ ≠ _fail_ __then return__ _h''_  
+ __return__ _fail_  
+
+---
+__Figure ??__ The current-best-hypothesis learning algorithm. It searches for a consistent hypothesis that fits all the examples and backtracks when no consistent specialization/generalization can be found. To start the algorithm, any hypothesis can be passed in; it will be specialized or generalized as needed.