Add adaboost markdown.

Author: ctjoreilly

README.md

@@ -76,7 +76,7 @@ The file [aima3e-algorithms.pdf](https://github.com/aimacode/pseudocode/blob/mas
 | • |  | [CROSS-VALIDATION-WRAPPER](md/Cross-Validation-Wrapper.md) |
 | • |  | [DECISION-LIST-LEARNING](md/Decision-List-Learning.md) |
 | • |  | [BACK-PROP-LEARNING](md/Back-Prop-Learning.md) |
-| • |  |  |
+| • |  | [ADABOOST](md/AdaBoost.md) |
 |<hr/>|<hr/>|<hr/>|
 | &bull; |  | [CURRENT-BEST-LEARNING](md/Current-Best-Learning.md) |
 | &bull; |  | [VERSION-SPACE-LEARNING](md/Version-Space-Learning.md) |

md/AdaBoost.md

@@ -0,0 +1,24 @@
+# ADABOOST
+
+## AIMA3e
+__function__ ADABOOST(_examples_, _L_, _K_) __returns__ a weighted\-majority hypothesis  
+&emsp;__inputs__: _examples_, set of _N_ labeled examples (_x<sub>1</sub>_, _y<sub>1</sub>_),&hellip;,(_x<sub>N</sub>_,_y<sub>N</sub>_)  
+&emsp;&emsp;&emsp;&emsp;_L_, a learning algorithm  
+&emsp;&emsp;&emsp;&emsp;_K_, the number of hypotheses in the ensemble  
+&emsp;__local variables__: __w__, a vector of _N_ example weights, initially 1 &frasl; _N_  
+&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;__h__, a vector of _K_ hypotheses  
+&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;__z__, a vector of _K_ hypothesis weights  
+
+&emsp;__for__ _k_ = 1 __to__ _K_ __do__  
+&emsp;&emsp;&emsp;__h__\[_k_\] &larr; _L_(_examples_, __w__)  
+&emsp;&emsp;&emsp;_error_ &larr; 0  
+&emsp;&emsp;&emsp;__for__ _j_ = 1 __to__ _N_ __do__  
+&emsp;&emsp;&emsp;&emsp;&emsp;__if__ __h__\[_k_\](_x<sub>j</sub>_) &ne; _y<sub>j</sub>_ __then__ _error_ &larr; _error_ &plus; __w__\[_j_\]  
+&emsp;&emsp;&emsp;__for__ _j_ = 1 __to__ _N_ __do__  
+&emsp;&emsp;&emsp;&emsp;&emsp;__if__ __h__\[_k_\](_x<sub>j</sub>_) = _y<sub>j</sub>_ __then__ __w__\[_j_\] &larr; __w__\[_j_\] &middot; _error_ &frasl; (1 &minus; _error_)  
+&emsp;&emsp;&emsp;__w__ &larr; NORMALIZE(__w__)  
+&emsp;&emsp;&emsp;__Z__\[_k_\] &larr; log(1 &minus; _error_) &frasl; _error_  
+&emsp; __return__ WEIGHTED\-MAJORITY(__h__, __z__)  
+
+---
+__Figure ??__ The ADABOOST variant of the boosting method for ensemble learning. The algorithm generates hypothesis by successively reweighting the training examples. The function WEIGHTED\-MAJORITY generates a hypothesis that returns the output value with the highest vote from the hypotheses in __h__, with votes weighted by __z__.