BIOMAT Database Logo BIOMAT Database Logo

Computational neural networks: enhancing supervised learning algorithms via self-organization. 1990

R M Holdaway, and M W White
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-7911.

A neural network processing scheme is proposed which utilizes a self-organizing Kohonen feature map as the front end to a feedforward classifier network. The results of a series of benchmarking studies based upon artificial statistical pattern recognition tasks indicate that the proposed architecture performs significantly better than conventional feedforward classifier networks when the decision regions are disjoint. This is attributed to the fact that the self-organization process allows internal units in the succeeding classifier network to be sensitive to a specific set of features in the input space at the outset of training.

UI MeSH Term Description Entries
D008959 Models, Neurological Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment. Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D001185 Artificial Intelligence Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language. AI (Artificial Intelligence),Computer Reasoning,Computer Vision Systems,Knowledge Acquisition (Computer),Knowledge Representation (Computer),Machine Intelligence,Computational Intelligence,Acquisition, Knowledge (Computer),Computer Vision System,Intelligence, Artificial,Intelligence, Computational,Intelligence, Machine,Knowledge Representations (Computer),Reasoning, Computer,Representation, Knowledge (Computer),System, Computer Vision,Systems, Computer Vision,Vision System, Computer,Vision Systems, Computer
D012815 Signal Processing, Computer-Assisted Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity. Digital Signal Processing,Signal Interpretation, Computer-Assisted,Signal Processing, Digital,Computer-Assisted Signal Interpretation,Computer-Assisted Signal Interpretations,Computer-Assisted Signal Processing,Interpretation, Computer-Assisted Signal,Interpretations, Computer-Assisted Signal,Signal Interpretation, Computer Assisted,Signal Interpretations, Computer-Assisted,Signal Processing, Computer Assisted

Related Publications

R M Holdaway, and M W White
Convergent on-line algorithms for supervised learning in neural networks.
January 2000, IEEE transactions on neural networks,
R M Holdaway, and M W White
MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks.
September 2022, Neural networks : the official journal of the International Neural Network Society,
R M Holdaway, and M W White
Supervised learning in spiking neural networks: A review of algorithms and evaluations.
May 2020, Neural networks : the official journal of the International Neural Network Society,
R M Holdaway, and M W White
Computational modeling of neural plasticity for self-organization of neural networks.
November 2014, Bio Systems,
R M Holdaway, and M W White
Self-Supervised Learning of Graph Neural Networks: A Unified Review.
February 2023, IEEE transactions on pattern analysis and machine intelligence,
R M Holdaway, and M W White
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning.
June 2022, IEEE transactions on neural networks and learning systems,
R M Holdaway, and M W White
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey.
November 2021, IEEE transactions on pattern analysis and machine intelligence,
R M Holdaway, and M W White
PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning.
October 2023, bioRxiv : the preprint server for biology,
R M Holdaway, and M W White
Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks.
February 2018, IEEE transactions on pattern analysis and machine intelligence,
R M Holdaway, and M W White
Novel maximum-margin training algorithms for supervised neural networks.
June 2010, IEEE transactions on neural networks,
Copied contents to your clipboard!