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We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences.
Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors.
(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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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 algorithm incrementally learns the residuals faults relationships and dynamically classifies the faults including multiple new classes.
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.
In this paper we propose a similarity measure for an attributed structural representation and an algorithm that incrementally learns the organizational structure of a case base.
In particular, this system includes perception capabilities that allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com