An Introduction to Machine Learning [E-Book] / by Miroslav Kubat.
This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, lin...
Saved in:
Full text |
|
Personal Name(s): | Kubat, Miroslav, author |
Imprint: |
Cham :
Springer International Publishing,
2015
|
Physical Description: |
XIII, 291 p. 71 illus., 2 illus. in color. online resource. |
Note: |
englisch |
ISBN: |
9783319200101 |
DOI: |
10.1007/978-3-319-20010-1 |
Subject (ZB): | |
Subject (LOC): | |
Classification: |
LEADER | 02490nam a22003975i 4500 | ||
---|---|---|---|
001 | 978-3-319-20010-1 | ||
003 | Springer | ||
008 | 150715s2015 gw | s |||| 0|eng d | ||
020 | |a 9783319200101 | ||
024 | 7 | |a 10.1007/978-3-319-20010-1 |2 doi | |
035 | |a (Sirsi) a682761 | ||
041 | |a eng | ||
082 | 0 | 4 | |a 006.3 |2 23 |
084 | 0 | |a DYA - Artificial intelligence | |
100 | 1 | |a Kubat, Miroslav, |e author | |
245 | 1 | 3 | |a An Introduction to Machine Learning |h [E-Book] / |c by Miroslav Kubat. |
264 | 1 | |a Cham : |b Springer International Publishing, |c 2015 |e (Springer LINK) |f SpringerComputerScience20180619 | |
300 | |a XIII, 291 p. 71 illus., 2 illus. in color. |b online resource. | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
347 | |a text file |b PDF |2 rda | ||
500 | |a englisch | ||
505 | 0 | |a A Simple Machine-Learning Task -- Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- Inter-Class Boundaries: Linear and Polynomial Classifiers -- Artificial Neural Networks -- Decision Trees -- Computational Learning Theory -- A Few Instructive Applications -- Induction of Voting Assemblies -- Some Practical Aspects to Know About -- Performance Evaluation.-Statistical Significance -- The Genetic Algorithm -- Reinforcement learning. | |
520 | |a This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of boosting, how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. | ||
596 | |a 1 | ||
650 | 0 | |a Artificial intelligence. | |
650 | 0 | |a Computer science. | |
650 | 0 | |a Computer simulation. | |
650 | 0 | |a Information storage and retrieval. | |
650 | 0 | |a Pattern recognition. | |
650 | 4 | |a Artificial intelligence | |
856 | 4 | 0 | |u http://dx.doi.org/10.1007/978-3-319-20010-1 |z Volltext |
915 | |a zzwFZJ3 | ||
932 | |a Computer Science (Springer-11645) | ||
949 | |a XX(682761.1) |w AUTO |c 1 |i 682761-1001 |l ELECTRONIC |m ZB |r N |s Y |t E-BOOK |u 19/6/2018 |x ZB-D |z UNKNOWN |0 NEL |1 ONLINE |