Changes

Jump to navigation Jump to search
no edit summary
|Has project status=Active
}}
 
== Text Processing==
 
There are two possible classification methods for the processing the text of target HTML pages. The first is a "Bag of Words" approach, which uses Term Frequency – Inverse Document Frequency to do basic natural language processing and select words or phrases which have discriminant capabilities. The second is a Word2Vec approach which uses shallow 2 layer neural networks to reduce descriptions to a vector with high discriminant potential. (See "Memo for Evan" in E:\mcnair\Projects\Incubators for further detail.)
== Main Tasks ==
# URL Crawler
E:\projects\listing page identifier\urlcrawler.py
 
=== Image Processing ===
 
This method would likely rely on a [https://en.wikipedia.org/wiki/Convolutional_neural_network convolutional neural network (CNN)] to classify HTML elements present in web page screenshots. Implementation could be achieved by combining the VGG16 model or ResNet architecture with batch normalization to increase accuracy in this context.

Navigation menu