Feature Selection Based on Iterative Orthogonal Experimental Design
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 95 === Feature selection is an important issue in the problem of machine learning. Especially in the domain of activity recognition, many researchers try to make use of multiple heterogeneous sensors and thus receive a large amount of signals. Many features can be extr...
Main Authors: | Shih-Chieh Yen, 顏士傑 |
---|---|
Other Authors: | Yung-jen Hsu |
Format: | Others |
Language: | en_US |
Published: |
2007
|
Online Access: | http://ndltd.ncl.edu.tw/handle/93997628014275251144 |
Similar Items
-
Iterative Algorithms Based on Orthogonal Arrays for Design of Experiments
by: Yi-Cheng Chen, et al.
Published: (2007) -
2-(Acetylsulfanyl)methyl Benzoate and 4-Acetoxybenzyl Carbonate: Two New Orthogonal Protecting Groups, and Selective Deprotection by Ytterbium(III) Triflate
by: Shih-Yao Yen, et al.
Published: (2019) -
Feature selection based on sequential orthogonal search strategy
by: Senawi, Azlyna
Published: (2018) -
Orthogonality Index Based Optimal Feature Selection for Visual Odometry
by: Huu Hung Nguyen, et al.
Published: (2019-01-01) -
Layer Assignment Based on Orthogonal Experimental Design
by: Jhang Cheng-Hao, et al.