Ebook: Data Complexity in Pattern Recognition

Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach.

This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks:

• What is missing from current classification techniques?

• When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task?

• How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data?

Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.

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Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach.

This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks:

• What is missing from current classification techniques?

• When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task?

• How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data?

Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.

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Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach.

This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks:

• What is missing from current classification techniques?

• When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task?

• How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data?

Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.

Content:
Front Matter....Pages I-XV
Front Matter....Pages 1-1
Measures of Geometrical Complexity in Classification Problems....Pages 1-23
Object Representation, Sample Size, and Data Set Complexity....Pages 25-58
Measures of Data and Classifier Complexity and the Training Sample Size....Pages 60-68
Linear Separability in Descent Procedures for Linear Classifiers....Pages 69-90
Data Complexity, Margin-Based Learning, and Popper’s Philosophy of Inductive Learning....Pages 91-114
Data Complexity and Evolutionary Learning....Pages 115-134
Classifier Domains of Competence in Data Complexity Space....Pages 135-152
Data Complexity Issues in Grammatical Inference....Pages 153-169
Front Matter....Pages 171-171
Simple Statistics for Complex Feature Spaces....Pages 173-195
Polynomial Time Complexity Graph Distance Computation for Web Content Mining....Pages 197-215
Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles....Pages 217-239
Complexity of Magnetic Resonance Spectrum Classification....Pages 241-248
Data Complexity in Tropical Cyclone Positioning and Classification....Pages 249-270
Human-Computer Interaction for Complex Pattern Recognition Problems....Pages 271-286
Complex Image Recognition and Web Security....Pages 287-298
Back Matter....Pages 299-300

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Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach.

This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks:

• What is missing from current classification techniques?

• When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task?

• How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data?

Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.

Content:
Front Matter....Pages I-XV
Front Matter....Pages 1-1
Measures of Geometrical Complexity in Classification Problems....Pages 1-23
Object Representation, Sample Size, and Data Set Complexity....Pages 25-58
Measures of Data and Classifier Complexity and the Training Sample Size....Pages 60-68
Linear Separability in Descent Procedures for Linear Classifiers....Pages 69-90
Data Complexity, Margin-Based Learning, and Popper’s Philosophy of Inductive Learning....Pages 91-114
Data Complexity and Evolutionary Learning....Pages 115-134
Classifier Domains of Competence in Data Complexity Space....Pages 135-152
Data Complexity Issues in Grammatical Inference....Pages 153-169
Front Matter....Pages 171-171
Simple Statistics for Complex Feature Spaces....Pages 173-195
Polynomial Time Complexity Graph Distance Computation for Web Content Mining....Pages 197-215
Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles....Pages 217-239
Complexity of Magnetic Resonance Spectrum Classification....Pages 241-248
Data Complexity in Tropical Cyclone Positioning and Classification....Pages 249-270
Human-Computer Interaction for Complex Pattern Recognition Problems....Pages 271-286
Complex Image Recognition and Web Security....Pages 287-298
Back Matter....Pages 299-300
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