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Probabilistic prediction using embed...

Kurwitz, Richard Cable.

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Probabilistic prediction using embedded random projections of high dimensional data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Probabilistic prediction using embedded random projections of high dimensional data./
作者:	Kurwitz, Richard Cable.
面頁冊數:	191 p.
附註:	Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5789.
Contained By:	Dissertation Abstracts International70-09B.
標題:	Engineering, Nuclear. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3370739
ISBN:	9781109363241

Probabilistic prediction using embedded random projections of high dimensional data.
Kurwitz, Richard Cable.

Probabilistic prediction using embedded random projections of high dimensional data. - 191 p.

Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5789.

Thesis (Ph.D.)--Texas A&M University, 2009.

The explosive growth of digital data collection and processing demands a new approach to the historical engineering methods of data correlation and model creation. A new prediction methodology based on high dimensional data has been developed. Since most high dimensional data resides on a low dimensional manifold, the new prediction methodology is one of dimensional reduction with embedding into a diffusion space that allows optimal distribution along the manifold. The resulting data manifold space is then used to produce a probability density function which uses spatial weighting to influence predictions i.e. data nearer the query have greater importance than data further away. The methodology also allows data of differing phenomenology e.g. color, shape, temperature, etc to be handled by regression or clustering classification.

ISBN: 9781109363241Subjects--Topical Terms:

1043651
Engineering, Nuclear.

Probabilistic prediction using embedded random projections of high dimensional data.
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245 1 0 $a Probabilistic prediction using embedded random projections of high dimensional data.
300 $a 191 p.
500 $a Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5789.
500 $a Adviser: Frederick R. Best.
502 $a Thesis (Ph.D.)--Texas A&M University, 2009.
520 $a The explosive growth of digital data collection and processing demands a new approach to the historical engineering methods of data correlation and model creation. A new prediction methodology based on high dimensional data has been developed. Since most high dimensional data resides on a low dimensional manifold, the new prediction methodology is one of dimensional reduction with embedding into a diffusion space that allows optimal distribution along the manifold. The resulting data manifold space is then used to produce a probability density function which uses spatial weighting to influence predictions i.e. data nearer the query have greater importance than data further away. The methodology also allows data of differing phenomenology e.g. color, shape, temperature, etc to be handled by regression or clustering classification.
520 $a The new methodology is first developed, validated, then applied to common engineering situations, such as critical heat flux prediction and shuttle pitch angle determination. A number of illustrative examples are given with a significant focus placed on the objective identification of two-phase flow regimes. It is shown that the new methodology is robust through accurate predictions with even a small number of data points in the diffusion space as well as flexible in the ability to handle a wide range of engineering problems.
590 $a School code: 0803.
650 4 $a Engineering, Nuclear. $3 1043651
650 4 $a Artificial Intelligence. $3 769149
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710 2 $a Texas A&M University. $3 718977
773 0 $t Dissertation Abstracts International $g 70-09B.
790 1 0 $a Best, Frederick R., $e advisor
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791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3370739