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Evaluation of hyperbox neural network learning for classification
Mark Eastwood,
Chrisina Jayne
SCEDT School Executive Team
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peer-review
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Computer Science
Neural Network
100%
Computational Efficiency
100%
Support Vector Machine
100%
Decision Trees
100%
Multilayer Perceptron
100%
Classification Problem
100%
Learning Problem
100%
Activation Function
100%
Attribute Value
100%
Supervised Learning
100%
Overlapping Region
100%
Supervised Classification
100%
Class Distribution
100%
Artificial Neural Network
100%
Mathematics
Concludes
100%
Neural Network
100%
Cluster Center
100%
Nearest Neighbor
100%
Multilayer Perceptron
100%
Support Vector Machine
100%
Classification Problem
100%
Decision Tree
100%
Artificial Neural Network
100%
Keyphrases
Neural Network Learning
100%
Hyperbox
100%
Artificial Data
25%
Support Vector Machine
12%
Computationally Efficient
12%
Computational Efficiency
12%
Decision Tree
12%
Learning Styles
12%
K-nearest
12%
Backpropagation
12%
Performance Efficiency
12%
Multilayer Perceptron
12%
Classification Problem
12%
Learning Problems
12%
Adaptive Activation Function
12%
Efficient Classification
12%
Supervised Classification
12%
Weight Vector
12%
Attribute Value
12%
Machine Decisions
12%
Supervised Learning
12%
Class Distribution
12%
Unsupervised Clustering
12%
Neural Network Algorithm
12%
Overlapping Region
12%
Cluster Center
12%
Chemical Engineering
Neural Network
100%
Multilayer Neural Networks
50%
Supervised Learning
50%
Support Vector Machine
50%