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Deep Learning With Conceptual View in Meta Data for Content Categorization

Deep Learning With Conceptual View in Meta Data for Content Categorization

Asokan R., Preethi P.
Copyright: © 2021 |Pages: 16
ISBN13: 9781799821083|ISBN10: 1799821080|ISBN13 Softcover: 9781799821090|EISBN13: 9781799821106
DOI: 10.4018/978-1-7998-2108-3.ch007
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MLA

R., Asokan, and Preethi P. "Deep Learning With Conceptual View in Meta Data for Content Categorization." Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., IGI Global, 2021, pp. 176-191. https://doi.org/10.4018/978-1-7998-2108-3.ch007

APA

R., A. & P., P. (2021). Deep Learning With Conceptual View in Meta Data for Content Categorization. In K. Senthilnathan, B. Shanmugam, D. Goyal, I. Annapoorani, & R. Samikannu (Eds.), Deep Learning Applications and Intelligent Decision Making in Engineering (pp. 176-191). IGI Global. https://doi.org/10.4018/978-1-7998-2108-3.ch007

Chicago

R., Asokan, and Preethi P. "Deep Learning With Conceptual View in Meta Data for Content Categorization." In Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., 176-191. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2108-3.ch007

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Abstract

Data gathered from various devices have to be observed by human operators manually for extended durations which is not viable and may lead to imprecise results. Data are analyzed only when any unwanted event occurs. Machine-learning technology powers many aspects of modern society, from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products. Machine-learning systems are used to identify objects in different forms of data. For decades, constructing a pattern-recognition, machine-learning system required careful engineering and domain expertise to design a feature extractor that transformed the raw data into a suitable internal representation, which the learning subsystem could detect patterns in the input by making use of and integrating ideas such as backpropagation, regularization, the softmax function, etc. This chapter will cover the importance of representations and metadata appendage and feature vector construction for the training deep models optimization.

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