Supplementary Material: Online Incremental Feature Learning with Denoising Autoencoders
نویسندگان
چکیده
Roughly speaking, this update rule is based on the following idea: increase the number of feature increments when the performance improves (i.e., the model is not at optimum), and decrease the number of feature increments when there is minimal or no performance improvement (i.e., the model has converged). From this intuition, we consider the following update rule (referred to as “update rule I”):
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