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EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features
Traditional model-free feature selection methods treat each feature independently while disregarding the interrelationships among features, which leads to relatively poor performance compared with the model-aware methods.
Brain-inspired Distributed Memorization Learning for Efficient Feature-free Unsupervised Domain Adaptation
Compared with gradient based artificial neural networks, biological neural networks usually show a more powerful generalization ability to quickly adapt to unknown environments without using any gradient back-propagation procedure.
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