Supervised learning deals with the problem of discovering models
from data as relationships between input and output attributes. Two
types of models are distinguished: regression models (for
continuous output attributes) and classification models (for
discrete output attributes). This thesis addresses both regression
and classification problems with an emphasis on new applications
and on presenting improved evolutionary techniques. Such techniques
include Gene Expression Programming (classical and its adaptive
version), Genetic Programming, and the hypernetwork model of
learning (classical and its evolutionary version). Such methods can
be successfully applied to many problems from various domains. This
thesis presents applications for symbolic regression for inverse
problems, quantum circuit design, modeling of dynamic processes,
and forecasting price movement.
General
Imprint: |
Lap Lambert Academic Publishing
|
Country of origin: |
Germany |
Release date: |
March 2012 |
First published: |
March 2012 |
Authors: |
Elena B. Utu
|
Dimensions: |
229 x 152 x 13mm (L x W x T) |
Format: |
Paperback - Trade
|
Pages: |
224 |
ISBN-13: |
978-3-8484-3479-4 |
Categories: |
Books >
Arts & Architecture >
Music >
General
Books >
Music >
General
|
LSN: |
3-8484-3479-2 |
Barcode: |
9783848434794 |
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