Neural Smithing – Supervised Learning in Feedforward Artificial Neural Networks
By Barbara Hammer. Pattern Analysis and Applications, 2001
368 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/38f24db7abe5c2a3ffe900a56142bf000c0a7672
How researchers see this paper
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In Artificial Intelligence
- Seyedali Mirjalili
- Andrew Lewis
- Max Mignotte
- Lazhar Khelifi
- Ali Asghar Heidari
- Pavel Trojovský
- Amir H. Gandomi
- Mohammad Dehghani
Citations in the corpus
- Let a biogeography-based optimizer train your Multi-Layer Perceptron (2014, background): “It may be divided into two types: supervised [53,74] and unsupervised [50,58] learning.”
- New constraints on oscillation parameters fromνeappearance andνμdisappearance in the NOvA experiment (2018, background): “The final step of the classifier is a multilayer perceptron [28, 29] that maps the learned features onto a set of normalized classification scores, which range over beam neutrino event hypotheses (νe CC, νμ CC, ντ CC, and NC) and a cosmogenic hypothesis.”
- Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “The intermediate layers (in the middle of the input and output) are often called “hidden layers” because they are not directly observable from the inputs and outputs of the system [32].”