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- W2766363025 abstract "Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different datasets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching cost-function. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks." @default.
- W2766363025 created "2017-11-10" @default.
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- W2766363025 date "2017-01-01" @default.
- W2766363025 modified "2023-09-23" @default.
- W2766363025 title "Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification" @default.
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- W2766363025 doi "https://doi.org/10.1007/978-3-319-68600-4_41" @default.
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