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
满意答案
将
g:netrw_keepdir
设置为0可以为你工作而不是尝试绝对路径吗? 这不完全是你想要的(我怀疑它是采用vim CWD并将其应用于netrw而不是其他方式),但是如果你可以使用netrw管理你的vim CWD,你的命令可能只是工作原样。编辑:请看
:help netrw-c
进行详细说明。 netrw中的c
命令可能就足够了。Well, as it turns out, when your cursor is on the
..
in the file list it considers that a directory.All I really needed to do was move the cursor into the banner area before trying to
mt
- ormt
from the parent directory.Whoops!
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