An MRP formulation for supervised learning: generalized temporal difference learning models
In traditional statistical learning, data points are usually assumed to be independently and identically distributed (i.i.d.) following an unknown probability distribution. This paper presents a contrasting viewpoint, perceiving data points as interconnected and employing a Markov reward process (MR...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
OpenReview
2024
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