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Implementation of Decision Tree Clas...

Pai, Karthika Satheesh.

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Implementation of Decision Tree Classification for Ultralow Power MEMS Acoustic Sensors.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Implementation of Decision Tree Classification for Ultralow Power MEMS Acoustic Sensors./
作者:	Pai, Karthika Satheesh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	65 p.
附註:	Source: Masters Abstracts International, Volume: 80-02.
Contained By:	Masters Abstracts International80-02.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10824376
ISBN:	9780438290518

Implementation of Decision Tree Classification for Ultralow Power MEMS Acoustic Sensors.
Pai, Karthika Satheesh.

Implementation of Decision Tree Classification for Ultralow Power MEMS Acoustic Sensors. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 65 p.

Source: Masters Abstracts International, Volume: 80-02.

Thesis (M.S.)--University of California, Davis, 2018.

This item must not be added to any third party search indexes.

Heightened interest in machine learning and artificial intelligence have made these fields a viable candidate for diverse applications. This thesis focuses on comparing and contrasting a few machine learning algorithms to classify sound spectra, coded as binary vectors, from a dataset provided by DARPA. Previous attempts to do so have yielded large codebases with subpar performance. This thesis focuses on building a classification algorithm called a decision tree that has high accuracy and superior performance in low power hardware environments. The decision tree is built and tested with MATLAB and ultimately deployed into a low power field programmable gate array (FPGA) after translation to Verilog. The decision tree appears to have an accuracy from 96 to 98 percent, while consuming only 17 milliwatts.

ISBN: 9780438290518Subjects--Topical Terms:

1567821
Computer Engineering.

Implementation of Decision Tree Classification for Ultralow Power MEMS Acoustic Sensors.
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520 $a Heightened interest in machine learning and artificial intelligence have made these fields a viable candidate for diverse applications. This thesis focuses on comparing and contrasting a few machine learning algorithms to classify sound spectra, coded as binary vectors, from a dataset provided by DARPA. Previous attempts to do so have yielded large codebases with subpar performance. This thesis focuses on building a classification algorithm called a decision tree that has high accuracy and superior performance in low power hardware environments. The decision tree is built and tested with MATLAB and ultimately deployed into a low power field programmable gate array (FPGA) after translation to Verilog. The decision tree appears to have an accuracy from 96 to 98 percent, while consuming only 17 milliwatts.
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