Algorithms that can facilitate incremental learning are known as incremental algorithms.
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Applying incremental learning to big data aims to produce faster classification or forecasting times.
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The more parents supported incremental learning the more the children were persistent on the task.
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Many traditional machine learning algorithms inherently support incremental learning, other algorithms can be adapted to facilitate this.
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It supports incremental learning whereby the classifier can be updated efficiently with information from new examples as they become available.
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The results conclude that reinforcing the words orthography might help readers recognize a word in future encounters which will influence the process of incremental learning.
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Similar results are shown in the paper " Incremental learning of object detectors using a visual shape alphabet ", yet the authors used AdaBoost for boosting.
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The aim of incremental learning is for the learning model to adapt to new data without forgetting its existing knowledge, it does not retrain the model.
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The incremental learning hypothesis is supported by the notion that awhile after Ai-Bi pairs are learned, the recall time to recall Bi decreases with continued learning trails.
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Both methods could be combined : the system could start with initial standard values of these parameters issued from a generic database, then some incremental learning customizes the classifier to each individual user.
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