Can we learn sequentially available data without retraining on previous datapoints?
We propose π’π₯ππΆπ (Orthogonal Recursive Fitting), an algorithm for "one-pass" learning which seeks to fit every new datapoint while minimally changing the predictions on previous data.
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We propose π’π₯ππΆπ (Orthogonal Recursive Fitting), an algorithm for "one-pass" learning which seeks to fit every new datapoint while minimally changing the predictions on previous data.
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