A comparative study of the scalability of a sensitivity-based learning algorithm for artificial neural networks.
Diego Peteiro-BarralBertha Guijarro-BerdiñasBeatriz Pérez-SánchezOscar Fontenla-RomeroPublished in: Expert Syst. Appl. (2013)
Keyphrases
- artificial neural networks
- learning algorithm
- back propagation
- learning rules
- neural network
- feed forward
- machine learning algorithms
- computational intelligence
- fault tolerance
- active learning
- genetic algorithm ga
- learning scheme
- using artificial neural networks
- high sensitivity
- sensitivity analysis
- comparative study
- reinforcement learning
- generalization ability
- scalability issues
- supervised learning
- hidden neurons
- learning rate
- training algorithm
- machine learning
- application of artificial neural networks
- high scalability
- scalable video coding
- multi layer perceptron
- generalization error
- fuzzy logic
- training data
- genetic algorithm
- rbf network
- sample complexity
- multi task learning
- hidden layer
- activation function
- learning models
- feed forward neural networks
- recurrent neural networks
- radial basis function
- training examples