Nicht aus der Schweiz? Besuchen Sie lehmanns.de

Knowledge Discovery with Support Vector Machines (eBook)

(Autor)

eBook Download: EPUB
2011 | 1. Auflage
262 Seiten
John Wiley & Sons (Verlag)
978-1-118-21103-8 (ISBN)

Lese- und Medienproben

Knowledge Discovery with Support Vector Machines - Lutz H. Hamel
Systemvoraussetzungen
117,99 inkl. MwSt
(CHF 115,25)
Der eBook-Verkauf erfolgt durch die Lehmanns Media GmbH (Berlin) zum Preis in Euro inkl. MwSt.
  • Download sofort lieferbar
  • Zahlungsarten anzeigen
An easy-to-follow introduction to support vector machines

This book provides an in-depth, easy-to-follow introduction to
support vector machines drawing only from minimal, carefully
motivated technical and mathematical background material. It begins
with a cohesive discussion of machine learning and goes on to
cover:

* Knowledge discovery environments

* Describing data mathematically

* Linear decision surfaces and functions

* Perceptron learning

* Maximum margin classifiers

* Support vector machines

* Elements of statistical learning theory

* Multi-class classification

* Regression with support vector machines

* Novelty detection

Complemented with hands-on exercises, algorithm descriptions,
and data sets, Knowledge Discovery with Support Vector
Machines is an invaluable textbook for advanced undergraduate
and graduate courses. It is also an excellent tutorial on support
vector machines for professionals who are pursuing research in
machine learning and related areas.

Lutz Hamel, PhD, teaches at the University of Rhode Island, where he founded the machine learning and data mining group. His major research interests are computational logic, machine learning, evolutionary computation, data mining, bioinformatics, and computational structures in art and literature.

Preface.

PART I.

1 What is Knowledge Discovery?

1.1 Machine Learning.

1.2 The Structure of the Universe X.

1.3 Inductive Learning.

1.4 Model Representations.

Exercises.

Bibliographic Notes.

2 Knowledge Discovery Environments.

2.1 Computational Aspects of Knowledge Discovery.

2.1.1 Data Access.

2.1.2 Visualization.

2.1.3 Data Manipulation.

2.1.4 Model Building and Evaluation.

2.1.5 Model Deployment.

2.2 Other Toolsets.

Exercises.

Bibliographic Notes.

3 Describing Data Mathematically.

3.1 From Data Sets to Vector Spaces.

3.1.1 Vectors.

3.1.2 Vector Spaces.

3.2 The Dot Product as a Similarity Score.

3.3 Lines, Planes, and Hyperplanes.

Exercises.

Bibliographic Notes.

4 Linear Decision Surfaces and Functions.

4.1 From Data Sets to Decision Functions.

4.1.1 Linear Decision Surfaces through the Origin.

4.1.2 Decision Surfaces with an Offset Term.

4.2 A Simple Learning Algorithm.

4.3 Discussion.

Exercises.

Bibliographic Notes.

5 Perceptron Learning.

5.1 Perceptron Architecture and Training.

5.2 Duality.

5.3 Discussion.

Exercises.

Bibliographic Notes.

6 Maximum Margin Classifiers.

6.1 Optimization Problems.

6.2 Maximum Margins.

6.3 Optimizing the Margin.

6.4 Quadratic Programming.

6.5 Discussion.

Exercises.

Bibliographic Notes.

PART II.

7 Support Vector Machines.

7.1 The Lagrangian Dual.

7.2 Dual MaximumMargin Optimization.

7.2.1 The Dual Decision Function.

7.3 Linear Support Vector Machines.

7.4 Non-Linear Support Vector Machines.

7.4.1 The Kernel Trick.

7.4.2 Feature Search.

7.4.3 A Closer Look at Kernels.

7.5 Soft-Margin Classifiers.

7.5.1 The Dual Setting for Soft-Margin Classifiers.

7.6 Tool Support.

7.6.1 WEKA.

7.6.2 R.

7.7 Discussion.

Exercises.

Bibliographic Notes.

8 Implementation.

8.1 Gradient Ascent.

8.1.1 The Kernel-Adatron Algorithm.

8.2 Quadratic Programming.

8.2.1 Chunking.

8.3 Sequential Minimal Optimization.

8.4 Discussion.

Exercises.

Bibliographic Notes.

9 Evaluating What has been Learned.

9.1 Performance Metrics.

9.1.1 The Confusion Matrix.

9.2 Model Evaluation.

9.2.1 The Hold-Out Method.

9.2.2 The Leave-One-Out Method.

9.2.3 N-Fold Cross-Validation.

9.3 Error Confidence Intervals.

9.3.1 Model Comparisons.

9.4 Model Evaluation in Practice.

9.4.1 WEKA.

9.4.2 R.

Exercises.

Bibliographic Notes.

10 Elements of Statistical Learning Theory.

10.1 The VC-Dimension and Model Complexity.

10.2 A Theoretical Setting for Machine Learning.

10.3 Empirical Risk Minimization.

10.4 VC-Confidence.

10.5 Structural Risk Minimization.

10.6 Discussion.

Exercises.

Bibliographic Notes.

PART III.

11 Multi-Class Classification.

11.1 One-versus-the-Rest Classification.

11.2 Pairwise Classification.

11.3 Discussion.

Exercises.

Bibliographic Notes.

12 Regression with Support Vector Machines.

12.1 Regression as Machine Learning.

12.2 Simple and Multiple Linear Regression.

12.3 Regression with Maximum Margin Machines.

12.4 Regression with Support Vector Machines.

12.5 Model Evaluation.

12.6 Tool Support.

12.6.1 WEKA.

12.6.2 R.

Exercises.

Bibliographic Notes.

13 Novelty Detection.

13.1 Maximum Margin Machines.

13.2 The Dual Setting.

13.3 Novelty Detection in R.

Exercises.

Bibliographic Notes.

Appendix A: Notation.

Appendix B: A Tutorial Introduction to R.

B.1 Programming Constructs.

B.2 Data Constructs.

B.3 Basic Data Analysis.

Bibliographic Notes.

References.

Index.

Erscheint lt. Verlag 20.9.2011
Reihe/Serie Wiley Series on Methods and Applications
Wiley Series on Methods and Applications
Sprache englisch
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Office Programme Outlook
Schlagworte Computer Science • Data Mining • Data Mining & Knowledge Discovery • Data Mining Statistics • Data Mining u. Knowledge Discovery • Informatik • Software engineering • Software-Engineering • Statistics • Statistik
ISBN-10 1-118-21103-0 / 1118211030
ISBN-13 978-1-118-21103-8 / 9781118211038
Haben Sie eine Frage zum Produkt?
EPUBEPUB (Adobe DRM)
Größe: 7,8 MB

Kopierschutz: Adobe-DRM
Adobe-DRM ist ein Kopierschutz, der das eBook vor Mißbrauch schützen soll. Dabei wird das eBook bereits beim Download auf Ihre persönliche Adobe-ID autorisiert. Lesen können Sie das eBook dann nur auf den Geräten, welche ebenfalls auf Ihre Adobe-ID registriert sind.
Details zum Adobe-DRM

Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belle­tristik und Sach­büchern. Der Fließ­text wird dynamisch an die Display- und Schrift­größe ange­passt. Auch für mobile Lese­geräte ist EPUB daher gut geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen eine Adobe-ID und die Software Adobe Digital Editions (kostenlos). Von der Benutzung der OverDrive Media Console raten wir Ihnen ab. Erfahrungsgemäß treten hier gehäuft Probleme mit dem Adobe DRM auf.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen eine Adobe-ID sowie eine kostenlose App.
Geräteliste und zusätzliche Hinweise

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Mehr entdecken
aus dem Bereich
Datenschutz und Sicherheit in Daten- und KI-Projekten

von Katharine Jarmul

eBook Download (2024)
O'Reilly Verlag
CHF 48,75