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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/88119

    Title: 一種應用鄰近關係的特徵擷取演算法
    Other Titles: A novel feature selection method based on the neighborhood relation
    Authors: 戴賢榜;Tai, Hsien-Pang
    Contributors: 淡江大學電機工程學系碩士班
    Keywords: 循序向前特徵選取法;循序向後特徵選取法;文字辨識;腦波分析;weight value;SFS;SBS;text categorization
    Date: 2012
    Issue Date: 2013-04-13 12:01:39 (UTC+8)
    Abstract: 近年來特徵選取逐漸應用於成千上萬的資料集中提取重要的特徵的領域中,這些領域包括:文字辨識、基因陣列、生醫信號分析。
    Recently, feature selection have been applied to many area which have housands of dataset. Those area is text categorization , microarrays , biomedical signal analysis.
    Feature selection in pattern recognition and machine learning to play a crucial role. Law in a number of feature selection method, the sequential forward search (SFS) and sequential backward search (SBS) is the most widely used method, the proposed method is based on proximity combine in sequential forward search method (SFS), as well as sequential backward search (SBS), we have developed a feature selection based on the concept of proximity, in accordance with the characteristics (one-dimensional data) before and after each feature or features (2D data), the relationship between up and down to give the distance weight value, ranking, based on its distance weight value according to the order to select the recognition rate can improve the characteristics, and then the rest of the unimportant features further be removed, so filter out the important feature subset in order to enhance the recognition rate, and characteristics will be gathered at a critical the area.
    In this paper, the experimental part we were had simulation test for character recognition, as well as rat brain waves in the rat brain waves for the rats awake state (AW), slow wave sleep (SWS) and rapid eye movement sleep (REM) brainwave state of the three acts of the state to do feature selection. In addition, character recognition, text "太", "大", "犬" experiment, respectively, as a group of "太"大"and"大","犬"a group of too, "太" and"大"and"犬"group. Finally the proposed method in this paper the above experiments to enhance the recognition rate and are looking out feature subset which fall in the critical region.
    Appears in Collections:[電機工程學系暨研究所] 學位論文

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