Titlebook: Sequential Decision-Making in Musical Intelligence; Elad Liebman Book 2020 Springer Nature Switzerland AG 2020 Computational Intelligence.

Irascible

太空

動(dòng)物

Algorithms for Tracking Changes in Preference Distributions,e broadly applicable to ML research in general. In this chapter I focus on the application of reinforcement learning approaches to handle concept drift, specifically through model retraining, both in its general context and directly with respect to tracking people’s temporal listening habits as reflected by a real world dataset.

正論

Infusion

cravat

Playlist Recommendation,joyment of music is one of the most common human activities. As an activity, music listening sessions commonly span over a sequence of songs, rather than a single song in isolation. Importantly, it is well established that music is experienced in temporal context and in sequence [., .].

angiography

Algorithms for Tracking Changes in Preference Distributions,nal properties of music, and people’s musical tastes, change over time in ways that are nontrivial to track and predict. This informational challenge is a special case of concept drift—the change, either abrupt or gradual, in the underlying structure of data. Concept drift is a common and fundamenta

機(jī)構(gòu)

interlude

Impact of Music on Person-Agent Interaction,ontrivial way, and that this effect can be modeled computationally. In this chapter I take this research thread two steps further: first to provide evidence that the impact of music on human decision-making goes beyond binary decisions; and second to show results suggesting that this effect can be m

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