Difference between revisions of "Industry Classifier"
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=Possible Tools= | =Possible Tools= | ||
| − | ==SciKit Learn SVM== | + | ==Python Tools== |
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| + | ===SciKit Learn SVM=== | ||
http://scikit-learn.org/stable/modules/svm.html#svm | http://scikit-learn.org/stable/modules/svm.html#svm | ||
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| − | ==SciKit Learn Neural Net== | + | ===SciKit Learn Neural Net=== |
http://scikit-learn.org/stable/modules/neural_networks_supervised.html | http://scikit-learn.org/stable/modules/neural_networks_supervised.html | ||
Revision as of 12:47, 8 February 2017
| Industry Classifier | |
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| Project Information | |
| Project Title | |
| Start Date | |
| Deadline | |
| Primary Billing | |
| Notes | |
| Has project status | |
| Copyright © 2016 edegan.com. All Rights Reserved. | |
Possible Tools
Python Tools
SciKit Learn SVM
http://scikit-learn.org/stable/modules/svm.html#svm
It's complexity is between O(n^2) and O(n^3). Seems easy to use. This is not a neural net; it is a support vector machine.
SciKit Learn Neural Net
http://scikit-learn.org/stable/modules/neural_networks_supervised.html
This IS a neural net using back propagation.
It's complexity is listed as: Suppose there are n training samples, m features, k hidden layers, each containing h neurons - for simplicity, and o output neurons. The time complexity of backpropagation is O(n * m * h^k * o * i), where i is the number of iterations. Since backpropagation has a high time complexity, it is advisable to start with smaller number of hidden neurons and few hidden layers for training.
Documentation is provided at:
https://scikit-neuralnetwork.readthedocs.io/en/latest/index.html
