- Full Description
Machines capable of automatic pattern recognition have many fascinating uses in science & 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. This book takes a close view of data complexity & its role in shaping the theories & techniques in different disciplines & 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? Uunique in its comprehensive coverage & multidisciplinary approach from various methodological & practical perspectives, researchers & practitioners will find this book an insightful reference to learn about current available techniques as well as application areas.
- Table of Contents
Table of Contents
- Theory and Methodology.
- Measures of Geometrical Complexity in Classification Problems.
- Object Representation, Sample Size and Dataset Complexity.
- Measures of Data and Classifier Complexity and the Training Sample Size.
- Linear Separability in Descent Procedures for Linear Classifiers.
- Data Complexity, Margin
- based Learning and Popper’s Philosophy of Inductive Learning.
- Data Complexity and Evolutionary Learning.
- Data Complexity and Domains of Competence of Classifiers.
- Data Complexity Issues in Grammatical Inference.
- Simple Statistics for Complex Feature Spaces.
- Polynomial Time for Complexity Graph Distance Computation for Web Content Mining.
- Data Complexity in Clustering Analysis for Gene Microarray Expression Profiles.
- Complexity of Magnetic Resonance Spectrum Classification.
- Data Complexity in Tropical Cyclone Positioning and Classification.
- Computer Interaction for Complex Pattern Recognition Problems.
- Complex Image Recognition and Web Security.
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