博碩士論文 107423036 完整後設資料紀錄

DC 欄位	值	語言
DC.contributor	資訊管理學系	zh_TW
DC.creator	曾欽緹	zh_TW
DC.creator	ChinTi Tseng	en_US
dc.date.accessioned	2020-7-16T07:39:07Z
dc.date.available	2020-7-16T07:39:07Z
dc.date.issued	2020
dc.identifier.uri	http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=107423036
dc.contributor.department	資訊管理學系	zh_TW
DC.description	國立中央大學	zh_TW
DC.description	National Central University	en_US
dc.description.abstract	機器學習是一門從數據中由電腦自行學習得出特徵，再利用特徵對未知數 據進行預測的技術。機器學習這門技術會因所針對的目標資料集不同，而設計 出對應的模型架構，也因此所需對應專業知識、所需花費的研究時間與資源甚 多，在普遍應用的期望下有一定的門檻與瓶頸。為了加速神經網路的建構 ，我們建構了一套基於演化演算法，結合深度學習技術，漸進式概念的建模演 算法，搭配經過設計的細胞結構，應用在運算資源稀缺的環境下，並在針對特 定資料集的背景下，自動搜尋出對應最優的神經網路架構。	zh_TW
dc.description.abstract	No matter designing a new neural network (NN) architectures or modifying an existed model require both human expertise and intense computational resources. We propose a progressive strategy to develop models on a “meta” level which recently arose interests of experts. This meta-modeling algorithm is based on evolutionary algorithms and deep learning techniques to generate NN architectures for a given task automatically. The work we did also includes encoding a model structure into many “cells” in a continual representation. Therefore, after defining the cell structure and its topology, we find the structures for the given task cell by cell, brick by brick, and find a structure which has the highest accuracy eventually.	en_US
DC.subject	機器學習	zh_TW
DC.subject	深度學習	zh_TW
DC.subject	神經架構搜尋	zh_TW
DC.subject	基因演算法	zh_TW
DC.subject	機器學習自動化	zh_TW
DC.subject	Machine Learning	en_US
DC.subject	Evolutionary Algorithm	en_US
DC.subject	Neural Architecture Search	en_US
DC.subject	Automated Machine Learning	en_US
DC.subject	Deep Learning	en_US
DC.title	以漸進式基因演算法實現神經網路架構搜尋最佳化	zh_TW
dc.language.iso	zh-TW	zh-TW
DC.title	A Progressive Genetic-based Optimization for Network Architecture Search	en_US
DC.type	博碩士論文	zh_TW
DC.type	thesis	en_US
DC.publisher	National Central University	en_US