Online Knowledge-Based Model for Big Data Topic Extraction
Joint Authors
Khan, Muhammad Taimoor
Durrani, Mehr
Aziz, Furqan
Khalid, Shehzad
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-19
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention.
Unlike traditional single-domain models they can easily scale up to explore big data.
The existing LML models have high data dependency, consume more resources, and do not support streaming data.
This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency.
With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces.
OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.
American Psychological Association (APA)
Khan, Muhammad Taimoor& Durrani, Mehr& Khalid, Shehzad& Aziz, Furqan. 2016. Online Knowledge-Based Model for Big Data Topic Extraction. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099719
Modern Language Association (MLA)
Khan, Muhammad Taimoor…[et al.]. Online Knowledge-Based Model for Big Data Topic Extraction. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1099719
American Medical Association (AMA)
Khan, Muhammad Taimoor& Durrani, Mehr& Khalid, Shehzad& Aziz, Furqan. Online Knowledge-Based Model for Big Data Topic Extraction. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099719
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099719