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Intelligent Human Computer Interaction - 12th International Conference, IHCI 2020, Daegu, South Korea, November 24-26, 2020, Proceedings, Part II (Paperback, 1st ed. 2021)
Madhusudan Singh, Dae-Ki Kang, Jong-Ha Lee, Uma Shanker Tiwary, Dhananjay Singh, …
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R1,609
Discovery Miles 16 090
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Ships in 10 - 15 working days
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The two-volume set LNCS 12615 + 12616 constitutes the refereed
proceedings of the 12th International Conference on Intelligent
Human Computer Interaction, IHCI 2020, which took place in Daegu,
South Korea, during November 24-26, 2020.The 75 full and 18 short
papers included in these proceedings were carefully reviewed and
selected from a total of 185 submissions. The papers were organized
in topical sections named: cognitive modeling and systems;
biomedical signal processing and complex problem solving; natural
language, speech, voice and study; algorithms and related
applications; crowd sourcing and information analysis; intelligent
usability and test system; assistive living; image processing and
deep learning; and human-centered AI applications.
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Intelligent Human Computer Interaction - 12th International Conference, IHCI 2020, Daegu, South Korea, November 24-26, 2020, Proceedings, Part I (Paperback, 1st ed. 2021)
Madhusudan Singh, Dae-Ki Kang, Jong-Ha Lee, Uma Shanker Tiwary, Dhananjay Singh, …
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R1,633
Discovery Miles 16 330
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Ships in 10 - 15 working days
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The two-volume set LNCS 12615 + 12616 constitutes the refereed
proceedings of the 12th International Conference on Intelligent
Human Computer Interaction, IHCI 2020, which took place in Daegu,
South Korea, during November 24-26, 2020.The 75 full and 18 short
papers included in these proceedings were carefully reviewed and
selected from a total of 185 submissions. The papers were organized
in topical sections named: cognitive modeling and system;
biomedical signal processing and complex problem solving; natural
language, speech, voice and study; algorithm and related
applications; crowd sourcing and information analysis; intelligent
usability and test system; assistive living; image processing and
deep learning; and human-centered AI applications.
In a typical inductive learning scenario, instances in a data set
are simply represented as ordered tuples of attribute values. In my
research, I explore three methodologies to improve the accuracy and
compactness of the classifiers: abstraction, aggregation, and
recursion. Firstly, abstraction is aimed at the design and analysis
of algorithms that generate and deal with taxonomies for the
construction of compact and robust classifiers. Secondly, I apply
aggregation method to constructively invent features in a multiset
representation for classification tasks. Finally, I construct a set
of classifiers by recursive application of weak learning
algorithms. Experimental results on various benchmark data sets
indicate that the proposed methodologies are useful in constructing
simpler and more accurate classifiers.
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