Classification methods in machine learning

This presentation focused on two types of machine learning-classification and clustering.  Machine learning uses algorithms to recognize patterns and make decisions based on data.  It is used in a wide arrary of fields-from statistics to artificial intelligence, and applied in many processes like object recognition, bioinformatics, and natural language processing.  Clustering techniques definitely  have a use for me, in regards to organizing genetic/genomic data.  If anyone has any resources on clustering techniques, I would be interested.

I found a really good article that compares different classification methods, as it is applied to clinical data:
http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=2232569&blobtype=pdf
Trying to extract meaningful information from narrative fields in medical databases can be difficult.  This articles compares different classification algorithms/methods, such as rule generation, decision trees, and Bayesian classifiers, when applied to the output of a natural language processor.  Extracting meaningful medical information was an ever-present issue in my last job, so it was interesting to see in which areas different algorithms performed better.  Overall, the article gave me more insight into mining narrative text.

Posted by Annie