106,99 €*
Versandkostenfrei per Post / DHL
Aktuell nicht verfügbar
It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory.
Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines.
From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory.
Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines.
From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
riggerRodrigo Fernandes de Mello is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil. He obtained his PhD degree from the University of São Paulo. His research interests include the Statistical Learning Theory, Machine Learning, Data Streams, and Applications in Dynamical Systems concepts. He has published more than 100 papers including journals and conferences, supported and organized international conferences, besides serving as Editor of International Journals.
Moacir Antonelli Ponti is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil, and was visiting researcher at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey. He obtained his PhD from the Federal University of São Carlos. His research interests include Pattern Recognition and Computer Vision, as well as Signal, Image and Video Processing.
This book includes a relevant discussion on Classification Algorithms as well as their source codes using the R Statistical Language
It also presents a very simple approach to understand the Statistical Learning Theory, which is considered a complex subject
Finally, it also discusses Kernels in a very user-friendly fashion
| Erscheinungsjahr: | 2018 |
|---|---|
| Fachbereich: | Anwendungs-Software |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xv
362 S. 190 s/w Illustr. 362 p. 190 illus. |
| ISBN-13: | 9783319949888 |
| ISBN-10: | 3319949888 |
| Sprache: | Englisch |
| Herstellernummer: | 978-3-319-94988-8 |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: |
Antonelli Ponti, Moacir
F Mello, Rodrigo |
| Auflage: | 1st ed. 2018 |
| Hersteller: | Springer International Publishing |
| Maße: | 241 x 160 x 26 mm |
| Von/Mit: | Moacir Antonelli Ponti (u. a.) |
| Erscheinungsdatum: | 13.08.2018 |
| Gewicht: | 0,735 kg |
riggerRodrigo Fernandes de Mello is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil. He obtained his PhD degree from the University of São Paulo. His research interests include the Statistical Learning Theory, Machine Learning, Data Streams, and Applications in Dynamical Systems concepts. He has published more than 100 papers including journals and conferences, supported and organized international conferences, besides serving as Editor of International Journals.
Moacir Antonelli Ponti is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil, and was visiting researcher at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey. He obtained his PhD from the Federal University of São Carlos. His research interests include Pattern Recognition and Computer Vision, as well as Signal, Image and Video Processing.
This book includes a relevant discussion on Classification Algorithms as well as their source codes using the R Statistical Language
It also presents a very simple approach to understand the Statistical Learning Theory, which is considered a complex subject
Finally, it also discusses Kernels in a very user-friendly fashion
| Erscheinungsjahr: | 2018 |
|---|---|
| Fachbereich: | Anwendungs-Software |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xv
362 S. 190 s/w Illustr. 362 p. 190 illus. |
| ISBN-13: | 9783319949888 |
| ISBN-10: | 3319949888 |
| Sprache: | Englisch |
| Herstellernummer: | 978-3-319-94988-8 |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: |
Antonelli Ponti, Moacir
F Mello, Rodrigo |
| Auflage: | 1st ed. 2018 |
| Hersteller: | Springer International Publishing |
| Maße: | 241 x 160 x 26 mm |
| Von/Mit: | Moacir Antonelli Ponti (u. a.) |
| Erscheinungsdatum: | 13.08.2018 |
| Gewicht: | 0,735 kg |
Ähnliche Produkte
Aktuell nicht verfügbar
Aktuell nicht verfügbar
Aktuell nicht verfügbar
Aktuell nicht verfügbar
Aktuell nicht verfügbar
Aktuell nicht verfügbar
