Difference between revisions of "Industry Classifier"
Jump to navigation
Jump to search
Peterjalbert (talk | contribs) (Created page with "{{McNair Projects |Project Title=Industry Classifier, |Start Term=Spring 2017, }}") |
Peterjalbert (talk | contribs) |
||
| Line 3: | Line 3: | ||
|Start Term=Spring 2017, | |Start Term=Spring 2017, | ||
}} | }} | ||
| + | |||
| + | |||
| + | ===Possible 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 | ||
Revision as of 12:46, 8 February 2017
| Industry Classifier | |
|---|---|
| Project Information | |
| Project Title | |
| Start Date | |
| Deadline | |
| Primary Billing | |
| Notes | |
| Has project status | |
| Copyright © 2016 edegan.com. All Rights Reserved. | |
Possible 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
