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In [14], behaviour primitives of the PHOENIX robot control architecture are incrementally learned from demonstrations.
Associative links to bind unimodal representations are incrementally learned by a semi-supervised algorithm with bidirectional connectivity.
The local structures of the appearance manifold are incrementally learned with changes in the appearance of the object.
One of the first approaches dealing with multimodality is proposed in [28], where a mixture of Gaussians is incrementally learned for each pixel.
This separation into a learning phase and retrieval phase is common in attractor neural network models in which the weights are incrementally learned [6, 35, 47], and there is experimental evidence supporting, at least in part, the use of two separate phases.
Models obtained from the bank of filters are incrementally learned by looking at abnormalities or deviations from a reference formulation based on a random walk behavior, such idea was first presented in [30], where an offline non-parametric method was proposed to characterize effects of static external objects in simulated environments.
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Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors.
We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences.
The algorithm incrementally learns the residuals faults relationships and dynamically classifies the faults including multiple new classes.
(d)–(f) We compute the reduced weights for a megamap that models an animal incrementally learning a square environment of increasing size [10].
This semi-supervised methodology allows us to build models based on weakly labelled data while incrementally learning latent positive and negative samples.
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