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Saturday, April 25 • 11:40am - 12:20pm
Classifying Text without (many) Labels

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Supervised text classification is often hampered by the need to acquire relatively expensive labeled training sets. In some embodiments of the systems and methods disclosed herein, pre-existing Word2Vec or similar algorithms are leveraged to create vector representations of documents that enable a model to be successfully trained with a drastically reduced training set. By using this technique the implementer can now devote low investment to acquiring a small volume of labeled data examples in order to train proximity thresholds, without devoting significant resources using traditional text classification machine learning algorithms which typically require training volume examples that are orders of magnitude larger.


Mike Tamir

@MikeTamirWith over a decade of teaching experience at the University level, Mike serves as CSO/Head of Education and Data Science Programing for Galvanize.  Mike also helped to found the GalvanizeU Masters program focused on developing the skills required of high performing Data... Read More →

Saturday April 25, 2015 11:40am - 12:20pm

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