Mitra Basu – författare

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.

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.

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.

This book constitutes the refereed proceedings of the 10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014, held in Zhangjiajie, China, in June 2014. The 33 revised full papers and 31 one-page abstracts included in this volume were carefully reviewed and selected from 119 submissions. The papers cover a wide range of topics in bioinformatics and computational biology and their applications including the development of experimental or commercial systems.