Now showing items 1-5 of 5
Novel document representations based on labels and sequential information
(Georgia Institute of Technology, 2015-07-23)
A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document ...
Disease progression modeling using multi-dimensional continuous-time hidden Markov model
(Georgia Institute of Technology, 2015-08-25)
Continuous-Time Hidden Markov Model (CT-HMM) is a useful tool for modeling disease progression from noisy observed data arriving irregularly in time. However, the lack of any widely-accepted efficient learning algorithm ...
Local approaches for collaborative filtering
(Georgia Institute of Technology, 2015-05-01)
Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for predicting a user's preference ...
Physics-based reinforcement learning for autonomous manipulation
(Georgia Institute of Technology, 2015-08-21)
With recent research advances, the dream of bringing domestic robots into our everyday lives has become more plausible than ever. Domestic robotics has grown dramatically in the past decade, with applications ranging from ...
New insights on the power of active learning
(Georgia Institute of Technology, 2015-07-22)
Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in the modern world has made unlabeled data much easier to ...