Statistical Pattern Recognition
(Sprache: Englisch)
Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences.
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Produktinformationen zu „Statistical Pattern Recognition “
Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences.
The book describes techniques for analysing data comprising measurements made on individuals or objects.. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set.
Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, including the analysis of complex networks and basic techniques for analysing the properties of datasets and also introduces readers to the use of variational methods for Bayesian density estimation and looks at new applications in biometrics and security.> The book describes techniques for analysing data comprising measurements made on individuals or objects.. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set.
Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, i
The book describes techniques for analysing data comprising measurements made on individuals or objects.. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set.
Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, including the analysis of complex networks and basic techniques for analysing the properties of datasets and also introduces readers to the use of variational methods for Bayesian density estimation and looks at new applications in biometrics and security.> The book describes techniques for analysing data comprising measurements made on individuals or objects.. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set.
Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, i
Klappentext zu „Statistical Pattern Recognition “
Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.
Statistical Pattern Recognition, 3rd Edition:
* Provides a self-contained introduction to statistical pattern recognition.
* Includes new material presenting the analysis of complex networks.
* Introduces readers to methods for Bayesian density estimation.
* Presents descriptions of new applications in biometrics, security, finance and condition monitoring.
* Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications
* Describes mathematically the range of statistical pattern recognition techniques.
* Presents a variety of exercises including more extensive computer projects.
The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals.
... mehr
Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.
www.wiley.com/go/statistical_pattern_recognition
www.wiley.com/go/statistical_pattern_recognition
... weniger
Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.
This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.
Statistical Pattern Recognition, 3rd Edition:
- Provides a self-contained introduction to statistical pattern recognition.
- Includes new material presenting the analysis of complex networks.
- Introduces readers to methods for Bayesian density estimation.
- Presents descriptions of new applications in biometrics, security, finance and condition monitoring.
- Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications
- Describes mathematically the range of statistical pattern recognition techniques.
- Presents a variety of exercises including more extensive computer projects.
The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.
www.wiley.com/go/statistical_pattern_recognitionhods for Bayesian density estimation.
- Presents descriptions of new applications in biometrics, security, finance and condition monitoring.
- Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications
- Describes mathematically the range of statistical pattern recognition techniques.
- Presents a variety of exercises including more extensive computer projects.
The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical
This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.
Statistical Pattern Recognition, 3rd Edition:
- Provides a self-contained introduction to statistical pattern recognition.
- Includes new material presenting the analysis of complex networks.
- Introduces readers to methods for Bayesian density estimation.
- Presents descriptions of new applications in biometrics, security, finance and condition monitoring.
- Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications
- Describes mathematically the range of statistical pattern recognition techniques.
- Presents a variety of exercises including more extensive computer projects.
The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.
www.wiley.com/go/statistical_pattern_recognitionhods for Bayesian density estimation.
- Presents descriptions of new applications in biometrics, security, finance and condition monitoring.
- Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications
- Describes mathematically the range of statistical pattern recognition techniques.
- Presents a variety of exercises including more extensive computer projects.
The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical
Inhaltsverzeichnis zu „Statistical Pattern Recognition “
- Preface- Acknowledgements
- Notation
1 Introduction to statistical pattern recognition
1.1 Statistical pattern recognition
1.2 Stages in a pattern recognition problem
1.3 Issues
1.4 Approaches to statistical pattern recognition
1.5 Elementary decision theory
1.6 Discriminant functions
1.7 Multiple regression
1.8 Outline of book
1.9 Notes and references Exercises
2 Density estimation _ parametric
2.1 Introduction
2.2 Estimating the parameters of the distributions
2.3 The Gaussian classifier
2.4 Dealing with singularities in the Gaussian classifier
2.5 Finite mixture models
2.6 Application studies
2.7 Summary and discussion
2.8 Recommendations
2.9 Notes and references Exercises 91
3 Density estimation _ Bayesian
3.1 Introduction
3.2 Analytic solutions normal distribution
3.3 Bayesian sampling schemes
3.4 Markov chain Monte Carlo methods
3.5 Bayesian approaches to discrimination
3.6 Sequential Monte Carlo Samplers
3.7 Variational Bayes
3.8 Approximate Bayesian Computation
3.9 Example application study
3.10 Application studies
3.11 Summary and discussion
3.12 Recommendations
3.13 Notes and references Exercises
4 Density estimation - nonparametric
4.1 Introduction
4.2 k-nearest-neighbour method
4.3 Histogram method
4.4 Kernel methods
4.5 Expansion by basis functions
4.6 Copulas
4.7 Application studies
4.8 Summary and discussion
4.9 Recommendations
4.10 Notes and references
5 Linear discriminant analysis
5.1 Introduction
5.2 Two-class algorithms
5.3 Multiclass algorithms
5.4 Support vector machines
5.5 Logistic discrimination
5.6 Application studies
5.7 Summary and discussion
5.8 Recommendations
5.9 Notes and references Exercises
6 Nonlinear discriminant analysis _ kernel and projection methods
6.1 Introduction
6.2 Radial basis functions
6.3 Nonlinear support vector machines
6.4 The multilayer perceptron
6.5 Application studies
6.6 Summary and discussion
6.7 Recommendations
6.8 Notes and
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references Exercises
7 Rule and decision tree induction
7.1 Introduction
7.2 Decision trees
7.3 Rule induction
7.4 Multivariate adaptive regression splines
7.5 Application studies
7.6 Summary and discussion
7.7 Recommendations
7.8 Notes and references Exercises
8 Ensemble methods
8.1 Introduction
8.2 Characterising a classifier combination scheme
8.3 Data fusion
8.4 Classifier combination methods
8.5 Application studies
8.6 Summary and discussion
8.7 Recommendations
8.8 Notes and references Exercises
9 Performance assessment
9.1 Introduction
9.2 Performance assessment
9.3 Comparing classifier performance
9.4 Application studies
9.5 Summary and discussion
9.6 Recommendations
9.7 Notes and references Exercises
10 Feature Selection and Extraction
10.1 Introduction
10.2 Feature selection
10.3 Linear feature extraction
10.4 Multidimensional scaling
10.5 Application studies
10.6 Summary and discussion
10.7 Recommendations
10.8 Notes and references Exercises
11 Clustering
11.1 Introduction
11.2 Hierarchical methods
11.3 Quick partitions
11.4 Mixture models
11.5 Sum-of-squares methods
11.6 Spectral clustering
11.7 Cluster validity
11.8 Application studies
11.9 Summary and discussion
11.10Recommendations
11.11Notes and references Exercises
12 Complex networks
12.1 Introduction
12.2 Mathematics of networks
12.3 Community detection
12.4 Link prediction
12.5 Application studies
12.6 Summary and discussion
12.7 Recommendations
12.8 Notes and references Exercises
13 Additional topics
13.1 Model selection
13.2 Missing data
13.3 Outlier detection and robust procedures
13.4 Mixed continuous and discrete variables
13.5 Structural risk minimisation and the VC dimension
- References
- Subject
- Index10.7 Recommendations
10.8 Notes and references Exercises
11 Clustering
11.1 Introduction
11.2 Hierarchical methods
11.3 Quick partitions
11.4 Mixture models
11.5 Sum-of-squares methods
11.6 Spectral clustering
11.7 Cluster validity
11.8 Application studies
11.9 Summary and discussion
11.10Recommendations
11.11Notes and references Exercises
12 Complex networks
12.1 Introduction
12.2 Mathematics of networks
12.3 Community detection
7 Rule and decision tree induction
7.1 Introduction
7.2 Decision trees
7.3 Rule induction
7.4 Multivariate adaptive regression splines
7.5 Application studies
7.6 Summary and discussion
7.7 Recommendations
7.8 Notes and references Exercises
8 Ensemble methods
8.1 Introduction
8.2 Characterising a classifier combination scheme
8.3 Data fusion
8.4 Classifier combination methods
8.5 Application studies
8.6 Summary and discussion
8.7 Recommendations
8.8 Notes and references Exercises
9 Performance assessment
9.1 Introduction
9.2 Performance assessment
9.3 Comparing classifier performance
9.4 Application studies
9.5 Summary and discussion
9.6 Recommendations
9.7 Notes and references Exercises
10 Feature Selection and Extraction
10.1 Introduction
10.2 Feature selection
10.3 Linear feature extraction
10.4 Multidimensional scaling
10.5 Application studies
10.6 Summary and discussion
10.7 Recommendations
10.8 Notes and references Exercises
11 Clustering
11.1 Introduction
11.2 Hierarchical methods
11.3 Quick partitions
11.4 Mixture models
11.5 Sum-of-squares methods
11.6 Spectral clustering
11.7 Cluster validity
11.8 Application studies
11.9 Summary and discussion
11.10Recommendations
11.11Notes and references Exercises
12 Complex networks
12.1 Introduction
12.2 Mathematics of networks
12.3 Community detection
12.4 Link prediction
12.5 Application studies
12.6 Summary and discussion
12.7 Recommendations
12.8 Notes and references Exercises
13 Additional topics
13.1 Model selection
13.2 Missing data
13.3 Outlier detection and robust procedures
13.4 Mixed continuous and discrete variables
13.5 Structural risk minimisation and the VC dimension
- References
- Subject
- Index10.7 Recommendations
10.8 Notes and references Exercises
11 Clustering
11.1 Introduction
11.2 Hierarchical methods
11.3 Quick partitions
11.4 Mixture models
11.5 Sum-of-squares methods
11.6 Spectral clustering
11.7 Cluster validity
11.8 Application studies
11.9 Summary and discussion
11.10Recommendations
11.11Notes and references Exercises
12 Complex networks
12.1 Introduction
12.2 Mathematics of networks
12.3 Community detection
... weniger
Bibliographische Angaben
- Autoren: Andrew R. Webb , Keith Derek Copsey , Gavin Cawley
- 2011, 3. Aufl., 512 Seiten, Masse: 17 x 24,4 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470682280
- ISBN-13: 9780470682289
- Erscheinungsdatum: 31.10.2011
Sprache:
Englisch
Rezension zu „Statistical Pattern Recognition “
"In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it." ( Zentralblatt MATH , 1 December 2012)
Pressezitat
"In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it." ( Zentralblatt MATH , 1 December 2012)Kommentar zu "Statistical Pattern Recognition"
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