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An adaptive document classifier insp...

Abi Haidar, Alaa.

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An adaptive document classifier inspired by T-cell cross-regulation in the immune system.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	An adaptive document classifier inspired by T-cell cross-regulation in the immune system./
作者:	Abi Haidar, Alaa.
面頁冊數:	178 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
Contained By:	Dissertation Abstracts International72-08B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3456435
ISBN:	9781124659329

An adaptive document classifier inspired by T-cell cross-regulation in the immune system.
Abi Haidar, Alaa.

An adaptive document classifier inspired by T-cell cross-regulation in the immune system. - 178 p.

Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .

Thesis (Ph.D.)--Indiana University, 2011.

Over millions of years, the vertebrate immune system has evolved into one of the most complex and intelligent biological systems. The immune system's function is to protect the body from harmful intruders. Several mathematical models have been proposed to understand the adaptive immune system and its functional subsystems. We develop a novel agent-based model of T-Cell cross-regulation in the adaptive immune system, and we apply it to binary classification problems analogous to those faced by the immune system.

ISBN: 9781124659329Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

An adaptive document classifier inspired by T-cell cross-regulation in the immune system.
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245 1 3 $a An adaptive document classifier inspired by T-cell cross-regulation in the immune system.
300 $a 178 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
500 $a Adviser: Luis M. Rocha.
502 $a Thesis (Ph.D.)--Indiana University, 2011.
520 $a Over millions of years, the vertebrate immune system has evolved into one of the most complex and intelligent biological systems. The immune system's function is to protect the body from harmful intruders. Several mathematical models have been proposed to understand the adaptive immune system and its functional subsystems. We develop a novel agent-based model of T-Cell cross-regulation in the adaptive immune system, and we apply it to binary classification problems analogous to those faced by the immune system.
520 $a We expect our study to help immunologists better understand the general mechanism behind T-cell cross-regulation and also to raise questions about the behavior of the adaptive immune system in general such as immune memory, cell death and homeostasis. However, our chief aim is to show that cross-regulation dynamics can be used to classify textual documents in changing corpora. We validate the model on real-world data from biomedical articles and personal e-mails, and we compare our algorithm with other machine learning classifiers. Finally, we discuss how the guiding of T-cell self-organizing dynamics can be seen as a general system of classification, the study of which is helpful for complex systems, text classification, and theoretical immunology.
590 $a School code: 0093.
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650 4 $a Health Sciences, Immunology. $3 1017716
650 4 $a Computer Science. $3 626642
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710 2 $a Indiana University. $b Informatics. $3 1680747
773 0 $t Dissertation Abstracts International $g 72-08B.
790 1 0 $a Rocha, Luis M., $e advisor
790 1 0 $a Menczer, Filippo $e committee member
790 1 0 $a Flammini, Alessandro $e committee member
790 1 0 $a Radivojac, Predrag $e committee member
790 $a 0093
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