Searched for: subject:"Interactive%5C+Learning"
(1 - 4 of 4)
document
Hammudo─člu, Joren (author)
Recommender systems are essential for filtering immense amounts of available digital content. As these quantities keep increasing, the impact of recommendations does so as well. In this work, we address negative impacts current state-of-the-art recommenders have. For the algorithmic filtering of items that are recommended to users, collaborative...
master thesis 2019
document
Scholten, Jan (author)
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems. Modelling and control design is longer required, which paves the way to numerous in- novations, such as optimal control of evermore sophisticated robotic systems, fast and efficient scheduling and logistics, effective personal drug dosing...
master thesis 2019
document
Wout, Daan (author)
A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is the necessity for extensive amounts of data for a suitable control policy. For systems that concern physical application, acquiring this vast amount of data might take an extraordinary amount...
master thesis 2019
document
Smits, E.A.P. (author)
We propose a method for identifying segments of a video that represent the events preferred by the user. Possible applications are personalized browsing through music DVDs or smart surveillance systems that can adapt to new circumstances. Requirements for this system are that it is generic and adaptable to the user and to new circumstances....
master thesis 2008
Searched for: subject:"Interactive%5C+Learning"
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