Active Learning for Image Recognition and Retrieval | Incremental Nonparametric Discriminant Analysis based Active Learning inspired by Cognitive Psychology

Kshitij Dhoble

ISBN 10: 3845406984 / ISBN 13: 9783845406985

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

Active Learning for Image Recognition and Retrieval | Incremental Nonparametric Discriminant Analysis based Active Learning inspired by Cognitive Psychology | Kshitij Dhoble | Taschenbuch | 128 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845406985 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 106642063

Bibliographic Details

• Title: Active Learning for Image Recognition and Retrieval | Incremental Nonparametric Discriminant Analysis based Active Learning inspired by Cognitive Psychology
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3845406984
• ISBN 13: 9783845406985
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 209 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms’ optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans’ innate cognitive learning ability. Active learning is one such approach.

About the Author

He has received his MCIS (Hons) degree in Computer and Information Sciences from Auckland University of Technology. He is currently a Ph.D. student. His research interests are in the areas of nature inspired neural network models, image/video processing and data mining techniques.