感知器学习 - 最重要的特征(Perceptron learning - most important feature)

 对于我在AI课程中的一项任务，我们的任务是创建Widrow Hoff delta规则的感知器学习实现。 我在java中编写了这个实现： 
 
 以下github链接包含该项目： https ： //github.com/dmcquillan314/CS440-Homework/tree/master/CS440-HW2-1 
 
 我遇到的问题不在于感知器的创造。 这工作正常。 
 
 在训练后的项目中，感知器然后将未分类的数据集应用于感知器，然后学习每个输入向量的分类。 这也很好。 
 
 我的问题涉及学习输入的哪个特征是最重要的。 
 
 例如，如果每个输入向量中的特征集是颜色，汽车模型和汽车制造，我们想要分类哪个特征是最重要的。 怎么会这样做呢。 
 
 我对此的原始理解使我相信计算相关系数是每个输入的特征值和产生的分类矢量。 然而，事实证明这是一个错误的假设。 
 
 还有其他方法可以学习最重要的功能吗？ 
 
 编辑 
 
 样本权重向量： 
 
 （-752,4771,17714,762,6,676,3060，-2004,5459,9591.299,3832,14963,20912） 
 
 样本输入向量： 
 
 （55,1,2,130,262,0,0,155,0,0,1,0,3,0） 
 
 （59,1,3,126,218,1,0,134,0,2.2,2,1,6,1） 
 
 （45,1,2,128,308,0,2,170,0,0,1,0,3,0） 
 
 （59,1,4,110,239,0,2,142,1,1.2,2,1,7,1） 
 
 最后一个要素是分类。 
 
 当我找到答案时，我会在这里发布答案。 到目前为止，我认为教师给出的答案是不准确的。 

For one of my assignments in my AI class we were tasked with creating a perceptron learning implementation of the Widrow Hoff delta rule. I've coded this implementation in java:
 
The following github link contains the project: https://github.com/dmcquillan314/CS440-Homework/tree/master/CS440-HW2-1
 
The issue that I'm having is not with the creation of the perceptron. That is working fine.
 
In the project after training the perceptron I then applied an unclassified dataset to the perceptron to then learn the classifications of each input vector. This also worked fine.
 
My issue pertains to learning which feature of the inputs is the most important.
 
For example, if the feature set within each input vector was color, car model, and car make and we wanted to classify which feature was the most important. How would one go about doing so.
 
My original understanding of this led me to believe that calculating the correlation coefficient the value of that feature for each input and the classification vector that is produced. However, this turned out to be a false assumption.
 
Is there some other way that the most important feature can be learned?
 
EDIT
 
Sample weight vector:
 
( -752, 4771, 17714, 762, 6, 676, 3060, -2004, 5459, 9591.299, 3832, 14963, 20912 )
 
Sample input vectors:
 
(55, 1, 2, 130, 262, 0, 0, 155, 0, 0, 1, 0, 3, 0)
 
(59, 1, 3, 126, 218, 1, 0, 134, 0, 2.2, 2, 1, 6, 1)
 
(45, 1, 2, 128, 308, 0, 2, 170, 0, 0, 1, 0, 3, 0)
 
(59, 1, 4, 110, 239, 0, 2, 142, 1, 1.2, 2, 1, 7, 1)
 
The last element is the classification.
 
I will post an answer here when I find one. So far I believe that the answer given by the instructor is inaccurate.

原文：https://stackoverflow.com/questions/21738277