missing value造句
例句與造句
- Represents a missing value in the
信息中的缺少值。 - Nulls are used in a database to indicate an unknown or missing value
Null用于在數(shù)據(jù)庫(kù)中指示未知或缺少的值。 - Missing value estimation for microarray expression data based on weighted regression
基因表達(dá)缺失值的加權(quán)回歸估計(jì)算法 - Study on processing method of missing values in personalized recommendation systems
個(gè)性化推薦系統(tǒng)中遺漏值處理方法的研究 - Or you might use the nil attribute defined in xml schemas to indicate a missing value
或者使用xml模式中定義的nil值來(lái)表示忽略的值。 - It's difficult to find missing value in a sentence. 用missing value造句挺難的
- Gets a value indicating whether the column contains non - existent or missing values
獲取一個(gè)值，該值指示列中是否包含不存在的或缺少的值。 - Gets a value indicating whether the column contains nonexistent or missing values
獲取一個(gè)值，用以表示列中是否包含不存在的或已丟失的值。 - Gets a value that indicates whether the column contains non - existent or missing values
獲取一個(gè)值，該值指示列中是否包含不存在的或缺少的值。 - Gets a value that indicates whether the column contains nonexistent or missing values
獲取一個(gè)值，該值指示列中是否包含不存在的或已丟失的值。 - Filling missing values , smoothing noise data and removing inconsistent data are all adopted to gain high quality data
通過(guò)補(bǔ)全缺失數(shù)據(jù)、平滑噪聲數(shù)據(jù)、消除不一致數(shù)據(jù)等技術(shù)，得到高質(zhì)量的數(shù)據(jù)。 - This approach deduces the missing value which makes the best of all information in time zone of missing point
建立前向灰預(yù)測(cè)和后向灰預(yù)測(cè)模型，充分利用缺失值時(shí)區(qū)窗口內(nèi)的全部信息對(duì)其進(jìn)行推理。 - The fuzzy lookup transformation performs data cleaning tasks such as standardizing data , correcting data , and providing missing values
模糊查找轉(zhuǎn)換執(zhí)行數(shù)據(jù)清理任務(wù)，例如標(biāo)準(zhǔn)化數(shù)據(jù)、更正數(shù)據(jù)以及提供丟失的值。 - The seasonal kendall test overcomes a number of problems that can commonly skew the results of long - term studies , such as non - normal data , missing values , seasonality and serial dependence where data is dependent on other data
這種方法可克服多種分析長(zhǎng)期性數(shù)據(jù)所出現(xiàn)的問(wèn)題，例如不正常數(shù)據(jù)數(shù)值缺失季節(jié)變化和數(shù)據(jù)相依某類數(shù)據(jù)依賴其他數(shù)據(jù)等因素。 - Firstly , influence factors of generalization of neural network are presented in this thesis , in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability , a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes , training epochs , initial weights , and so on ; secondly , through establishing integrating neural network and introducing data fusion technique , the integrality and precision of acquired knowledge is greatly improved . then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement , knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system , by completing missing value data and eliminating unnecessary attributes , discretization of continuous attribute , reducing redundancy , extracting rules in this thesis . finally , rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge , in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement , and rnn can improve the speed and quality of knowledge acquirement greatly
本文首先討論了影響神經(jīng)網(wǎng)絡(luò)的泛化能力的因素,提出了一種新的結(jié)構(gòu)自適應(yīng)神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法,在新方法中,采用了遺傳算法對(duì)神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)參數(shù)(隱層節(jié)點(diǎn)數(shù)、訓(xùn)練精度、初始權(quán)值)進(jìn)行優(yōu)化,大大提高了神經(jīng)網(wǎng)絡(luò)的泛化能力和知識(shí)動(dòng)態(tài)獲取自適應(yīng)能力;其次,構(gòu)造集成神經(jīng)網(wǎng)絡(luò),引入數(shù)據(jù)融合算法,實(shí)現(xiàn)了基于集成神經(jīng)網(wǎng)絡(luò)的融合診斷,有效地提高了知識(shí)獲取的全面性、完善性及精度;然后,針對(duì)知識(shí)獲取過(guò)程中所存在的不確定性、不完備性等問(wèn)題,探討了運(yùn)用粗糙集理論的知識(shí)獲取方法,通過(guò)缺損數(shù)據(jù)補(bǔ)齊、連續(xù)數(shù)據(jù)的離散、沖突消除、冗余信息約簡(jiǎn)、知識(shí)規(guī)則抽取等一系列的算法實(shí)現(xiàn)了智能診斷的知識(shí)規(guī)則獲取;最后,將粗糙集理論與神經(jīng)網(wǎng)絡(luò)相結(jié)合,研究了粗糙集-神經(jīng)網(wǎng)絡(luò)的知識(shí)獲取方法。 - This paper also studies in detail the problem of building concept lattice , and two efficient algorithms are developed . moreover , several extended model of concept lattice are presented to handle the problems in data processing , such as the missing value and the structured domain of attribute
此外，本文還對(duì)概念格的快速生成算法進(jìn)行了深入的研究，提出了一些高效的算法，文章的最后提出了幾種概念格擴(kuò)展模型，處理了數(shù)據(jù)中可能出現(xiàn)的缺值和結(jié)構(gòu)化屬性值域的問(wèn)題。
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