By Topic

Embracing Uncertainty: The New Machine Learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)

Summary form only given. Computers are based on logic, but must increasingly deal with real-world data that is full of uncertainty and ambiguity. Modern approaches to machine learning use probability theory to quantify and compute with this uncertainty, and have led to a proliferation in the applications of machine learning, ranging from recommendation systems to web search, and from spam filters to voice recognition. Most recently, the Kinect 3D full-body motion sensor, which has become the fastest-selling consumer electronics product in history, relies crucially on machine learning. Furthermore, the advent of widespread internet connectivity, with centralised data storage and processing, coupled with recently developed algorithms for computationally efficient probabilistic inference, will create many new opportunities for machine learning over the coming years. The talk will be illustrated with tutorial examples, demonstrations, and real-world case studies.

Published in:

Computer Software and Applications Conference (COMPSAC), 2011 IEEE 35th Annual

Date of Conference:

18-22 July 2011