The first lecture's emphasis was placed on the importance of semantics-with examples of the semiotic triange, the semantic web, and various web ontology languages. The semiotic triange reference struck me as familiar because I could remember work instances in which the particular example that Dr. Fridsma gave us of a protocol represented different meanings to different people. Even in the same working environment, different people place meaning of an object in different ways, due to past/present experiences. The current state of things on the web places more importance on syntax (the exchange of information) rather on semantics (the use of that information in a meaningful way). The ideal situation is to implement more semantics into the way the web operates so that it more accurately represents what people are trying to achieve.
The continutation of the machine learning lecture focused on two main types of clustering-k means and hierarchical. Clustering has implications in many types of information processing, including neural networking and bioinformatics. As I can see, my classmates have done a great job of defining all the clustering methods (way to go everyone!), so I will just list a site where you can see clustering applied to gene expression data. It is from the Center for Information Technology-National Institutes of Health and is a good summary of their research and goals for the future pertaining to clustering on larger datasets (which is much needed!).
http://cit.nih.gov/NR/exeres/DBC46094-ECE8-4419-AE65-48E041CECCB1,frameless.htm
Posted by Annie
No comments:
Post a Comment
Gentle Reminder: Sign comments with your name.