NeuroComputing Flashcards
The 5 areas associated with Supervised learning with neural networks
Biological Inspiration Artificial Neural Networks Multilayer perception Radical Basis Function Networks Support Vector Machines
The 7 parts associated with unsupervised learning
Self Organising maps SOM Algorithms Implementing a SOM algorithm Classificaiton with SOMs Self Organising swarm SOS swarm and SOM Adaptive Resonance Theory
the 4 parts of Neuro Evolution
Direct Encodings
NEAT
Indirect encodings
Other Hybrid Neural Algorithms
Artifical neural networks
Architectures
Multilayered Perception parts
Transfer function
Project construction and response regions
Relationships of MLPS to regression models
training an MLP
overtraining
practical issues in modelling and training
stacking MLPs
recurrent Networks
What does MLP stand for>
Multilayered perception
Radial Basis Function Networks
Kernal Functions Radial Basis Functions Intuition behind RBFs Training RBFs developing them RBFs
Support vector machines
Method
Issues in applications
Adaptive Resonance Theory
Unsupervised learning
Supervised learning
weaknesses of the ART approach
Direct encodings with neuro evolution
Weight Vectors Section of inputs Connect structure Hybrid MLP approaches Problems with the approach
NEAT
Representation
Diversity generation
Specification
Incremental Evolution
