© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
INFO331
Machine learning. Neural
networks. Supervised learning in
neural networks.MLP and BP
(Text book: section 2.11, pp.146

155; section
3.7.3., pp.218

221); section 4.2, pp.267

282;catch

up reading: pp.251

266)
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Machine learning
Issues in machine learning
Learning from static versus learning from
dynamic data
Incremental learning
On

line learning, adaptive learning
Life

long learning
Cognitive learning processes in humans
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Inductive learning
learning from examples
Inductive decision trees and the ID3
algorithm
Information gain evaluation
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Other methods of machine
learning
Learning by doing
Learning from advice
Learning by analogy
Case

based learning and reasoning
Template

based learning (Kasabov and
Clarke)

Iris example
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Learning fuzzy rules from data
Cluster

based methods
Fuzzy template

based method
(Kasabov, 96), pp.218

219
Wang’s method (pp.220

221)
Advantages and disadvantages
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Supervised learning in neural
networks
Supervised learning in neural networks
Perceptrons
Multilayer perceptrons (MLP) and the
backpropagation algorithm
MLP as universal approximators
Problems and features of the MPL
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Supervised learning in neural
networks
The learning principle is to provide the input
values and the desired output values for each
of the training examples.
The neural network changes its connection
weights during training.
Calculate the error:
•
training error

how well a NN has learned the data
•
test error

how well a trained NN generalises over
new input data.
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Perceptrons
fig.4.8
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Perceptrons
fig.4.9
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Perceptrons
fig.4.10
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
MLP and the backpropagation
algorithm
fig.4.11
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
MLP and the backpropagation
algorithm
fig.4.12
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
MLP and the backpropagation
algorithm
fig.4.13
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
MLPs as statistical tools
A MLP with one hidden layer can
approximate any continuous function to
any desired accuracy (Hornik et al,
1989)
MLP are multivariate non

linear
regression models
MLP can learn conditional probabilities
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Problems and features of the
MPL
How to chose the number of the hidden
nodes
Catastrophic forgetting
Introducing hints in neural networks
Overfitting (overlearning)
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Problems and features of the
MPL
Catastrophic forgetting
fig. 4.14
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Problems and features of the
MPL
Introducing hints
fig.4.15
© N. Kasabov Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Problems and features of the
MPL
Overfitting
fig. 4.16
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