data preprocessing中文是什么意思

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中文翻譯
造句

數(shù)據(jù)預(yù)處理

data n. 1.資料，材料〔此詞系 datum 的復(fù)數(shù)。但 d ...
data acquisition and preprocessing 數(shù)據(jù)采集和預(yù)處理
seismic data preprocessing 地震數(shù)據(jù)預(yù)處理
preprocessing 預(yù)處理; 預(yù)加工
digital preprocessing 數(shù)字預(yù)處理
image preprocessing 圖像預(yù)處理
intelligent preprocessing 智能預(yù)處理
linear preprocessing 線性預(yù)處理
preprocessing aid 預(yù)操作助劑
preprocessing directive 前置處理器指示詞
preprocessing expressions 預(yù)處理表達(dá)式
preprocessing program 預(yù)處理程序
preprocessing subprogram 預(yù)處理子程序
preprocessing unit 預(yù)處理機(jī); 預(yù)處理器
table preprocessing 表預(yù)處理
field preprocessing system 野外預(yù)處理系統(tǒng)
image preprocessing system 圖像預(yù)處理系統(tǒng)
preprocessing high-speed memory 預(yù)處理高速存儲器
seismic preprocessing system 地震預(yù)處理系統(tǒng)
data n. 1.資料，材料〔此詞系 datum 的復(fù)數(shù)。但 datum 罕用，一般即以 data 作為集合詞，在口語中往往用單數(shù)動(dòng)詞；如系指一件資料，則說作 this data〕。 2.〔美國〕(觀察所得的)事實(shí)，知識。 a data book 參考資料書。 gather data on ...收集…的資料[數(shù)據(jù)]。 The data is not enough to be convincing. 資料不足，尚難令人信服。
data in 輸入數(shù)據(jù); 數(shù)據(jù)輸入
data-in 記入數(shù)據(jù); 輸入數(shù)據(jù); 輸入諸元; 數(shù)據(jù)輸入
for data 循環(huán)數(shù)據(jù)
no data 無數(shù)據(jù)
on data 昂達(dá)

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例句與用法

Then , in order to resolve the problem of network delay , this paper studied the transaction recognition step of data preprocessing and proposed an improved time - window based transaction recognition method
同時(shí)，本文分析了數(shù)據(jù)準(zhǔn)備中的事務(wù)識別步驟，針對網(wǎng)絡(luò)延時(shí)的問題，提出了改進(jìn)的基于時(shí)間窗口的事務(wù)識別方法。
We expatiate such primary question as the goal , rules to design adaptive web site and collection of sourse of data for mining . we describe the algorithm and processes of user access pattern , and presents a data preprocessing model
給出了系統(tǒng)體系結(jié)構(gòu)，闡述了系統(tǒng)實(shí)現(xiàn)的目標(biāo)、設(shè)計(jì)所遵循的原則和源數(shù)據(jù)的收集等主要問題描述了系統(tǒng)挖掘用戶訪問模式的過程和算法。
Because the current business process system contain various data source in tax administration system , this thesis comprehensively gives to the strategy of realizing concerning to data cleaning , integration , transformation and data load in data preprocessing
由于在稅務(wù)系統(tǒng)當(dāng)前的業(yè)務(wù)處理系統(tǒng)有多種數(shù)據(jù)源，本文重點(diǎn)給出了實(shí)現(xiàn)數(shù)據(jù)預(yù)處理中的數(shù)據(jù)清理、集成、轉(zhuǎn)換和數(shù)據(jù)裝載的相應(yīng)策略。
Then , the conception of smart sensor is introduced for multi - sensor system design to improve systemic characteristic including : flexibility , universality , extendability , extendability , data preprocessing , self - adjustability , data transmission by bus , and so on
然后本文提出了多傳感器系統(tǒng)的底層傳感器模塊應(yīng)具有通用性、可擴(kuò)展性、對上層接口透明、開發(fā)傳承性、初級預(yù)處理和基于總線的數(shù)據(jù)傳輸?shù)仍O(shè)計(jì)要求。
In this thesis , the whole process of reverse engineering is integrated and completed , including data collecting , data preprocessing , curve fitting , surface reconstructing , mold designing and fast manufacturing . the main concepts of the thesis are followed : 1
論文從逆向工程集成的角度出發(fā),對基于線結(jié)構(gòu)激光測量的曲面數(shù)據(jù)采集及預(yù)處理、曲線擬合、曲面重建,及在此基礎(chǔ)上進(jìn)行的注塑模具設(shè)計(jì)及快速制造理論與應(yīng)用進(jìn)行了研究與實(shí)踐。
Data preprocessing refers to the process of polygonization , smoothing , decimation and registration . the flow and main steps of data preprocessing are described here . several specific cases , for example the sampling method based on chordal deviation , the feature based registration , are discussed
本文研究和討論了點(diǎn)云預(yù)處理的基本流程和主要步驟，研究點(diǎn)云光順的高斯濾波，中值濾波和平均濾波方法、點(diǎn)云精簡的弦高差算法和多視點(diǎn)云特征對齊的方法。
In this thesis , we study and discuss deeply data preprocessing based on rough set . first , the characteristic and shortcoming of primary algorithms about the computation of null values is analysised , and principle and target of the completation is indicated
而經(jīng)典的粗集理論不能處理原始數(shù)據(jù)資料中的遺漏信息以及值域?yàn)閷?shí)數(shù)的數(shù)據(jù)，需要經(jīng)過數(shù)據(jù)預(yù)處理，包括數(shù)據(jù)補(bǔ)齊和離散化處理后才能進(jìn)行知識獲取，因此如何有效地進(jìn)行數(shù)據(jù)預(yù)處理具有非常重要的現(xiàn)實(shí)意義。
Author also discusses how to improve the generalization of neural network . the two methods for improving generalization , " regularization " and " early stopping " are presented . the data preprocessing techniques , which can improve the efficiency of network training , are also discussed
論述了bp神經(jīng)網(wǎng)絡(luò)在應(yīng)用中的問題，包括提高神經(jīng)網(wǎng)絡(luò)的泛化能力的兩種方法：規(guī)則化方法和訓(xùn)練早期停止方法，以及各種數(shù)據(jù)預(yù)處理方法和神經(jīng)網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)的確定問題等。
After analyzing the desulfuration techniques , this dissertation optimizes the desulfuration static model through the effective data preprocessing and generalized genetic algorithm . the performance of the improved model proves better than traditional rbf model by the experiments
本文在分析脫硫工藝的基礎(chǔ)上，通過有效的數(shù)據(jù)預(yù)處理和廣義遺傳算法，優(yōu)化了rbf神經(jīng)網(wǎng)絡(luò)脫硫靜態(tài)模型，通過測試，模型預(yù)報(bào)精度好于傳統(tǒng)rbf神經(jīng)網(wǎng)絡(luò)模型，具有一定的實(shí)用性和推廣性。
Applying the interactive volume rendering algorithm as the core technology and combining other computer visualization and image processing technologies , a way of visualizing the volume data obtained by confocal microscopy has been set up . related techniques include confocal data preprocessing , rendering the specimens " surface structure by the marching cube algorithm , rendering the specimens " internal structure by the interactive volume rendering algorithm , 4d dynamic visualization and a series of interactive technologies . furthermore a system dataset is built to manage volume data , preprocess results , rendering results and analytic results
本研究以交互體繪制算法為核心并結(jié)合其它三維可視化技術(shù)和圖像處理技術(shù)，形成了一整套比較完善的共聚焦圖像可視化的方法，包括：共聚焦數(shù)據(jù)的預(yù)處理、基于marchingcubes算法的樣本表面繪制、基于交互體繪制算法的樣本內(nèi)部結(jié)構(gòu)的可視化、四維動(dòng)態(tài)可視化以及一系列交互分析手段，同時(shí)采用系統(tǒng)數(shù)據(jù)庫對體數(shù)據(jù)及其預(yù)處理結(jié)果、可視化結(jié)果和交互分析結(jié)果進(jìn)行管理，從而建立了共聚焦顯微圖像三維可視化與分析系統(tǒng)。

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