文件名称:active-learning-code
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Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量
activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量
getlabel.m:根据树和测试样例，得到根据该树得到的类标
getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加)
RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树，
RandomCreateTree_d_all.m:根据所有样本点的割点建造树
randomdata.m：给定属性取值，造数据
randomselect.m:从数据中随机选择一部分作为
showTree.m:显示树的结构
test.m:给出树，测试样例，得到正确率-Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate
activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量
getlabel.m:根据树和测试样例，得到根据该树得到的类标
getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加)
RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树，
RandomCreateTree_d_all.m:根据所有样本点的割点建造树
randomdata.m：给定属性取值，造数据
randomselect.m:从数据中随机选择一部分作为
showTree.m:显示树的结构
test.m:给出树，测试样例，得到正确率-Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate
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下载文件列表
代码\test.m
....\getTrees.m
....\RandomCreateTree_d_all.m
....\showTree.m
....\getlabel.m
....\randomdata.m
....\Learning_random.m
....\randomselect.m
....\activeLearning.m
....\RandomCreateTree_d_unbalance.m
....\readme.txt
数据\iris.data
....\treedata_iris.txt
....\traindata_iris.txt
....\testdata_iris.txt
....\result_iris_random2tree.txt
....\matlab_iris.mat
....\result_iris_random7sample.txt
....\readme.txt
....\复件 result_iris_random2tree.txt
....\总结.txt
....\复件 result_iris_random7sample.txt
代码
数据