This book not only discusses the important topics in the area of
machine learning and combinatorial optimization, it also combines
them into one. This was decisive for choosing the material to be
included in the book and determining its order of presentation.
Decision trees are a popular method of classification as well as of
knowledge representation. At the same time, they are easy to
implement as the building blocks of an ensemble of classifiers.
Admittedly, however, the task of constructing a near-optimal
decision tree is a very complex process. The good results typically
achieved by the ant colony optimization algorithms when dealing
with combinatorial optimization problems suggest the possibility of
also using that approach for effectively constructing decision
trees. The underlying rationale is that both problem classes can be
presented as graphs. This fact leads to option of considering a
larger spectrum of solutions than those based on the heuristic.
Moreover, ant colony optimization algorithms can be used to
advantage when building ensembles of classifiers. This book is a
combination of a research monograph and a textbook. It can be used
in graduate courses, but is also of interest to researchers, both
specialists in machine learning and those applying machine learning
methods to cope with problems from any field of R&D.
General
| Imprint: |
Springer Nature Switzerland AG
|
| Country of origin: |
Switzerland |
| Series: |
Studies in Computational Intelligence, 781 |
| Release date: |
February 2019 |
| First published: |
2019 |
| Authors: |
Jan Kozak
|
| Dimensions: |
235 x 155 x 9mm (L x W x T) |
| Format: |
Paperback
|
| Pages: |
159 |
| Edition: |
Softcover reprint of the original 1st ed. 2019 |
| ISBN-13: |
978-3-03-006716-8 |
| Categories: |
Books >
Computing & IT >
Applications of computing >
Artificial intelligence >
General
|
| LSN: |
3-03-006716-5 |
| Barcode: |
9783030067168 |
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