Abstract— Least-squares policy iteration is a useful reinforcement learning method in robotics due to its computational efficiency. However, it tends to be sensitive to outliers...
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spect...
Tracking-by-detection is increasingly popular in order to tackle the visual tracking problem. Existing adaptive methods suffer from the drifting problem, since they rely on selfup...
Jakob Santner, Christian Leistner, Amir Saffari, T...
Martinetz and Schulten proposed the use of a Competitive Hebbian Learning (CHL) rule to build Topology Representing Networks. From a set of units and a data distribution, a link i...
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...