noisy data 中文意思是什麼
noisy data
解釋
噪聲數據-
A neural network - based on substructural identification was presented for the estimation of the parameters of a complex structural system, particularly for the case with noisy and incomplete measurement of the modal data
應用神經網路和動態子結構模型完成復雜結構的參數識別，尤其考慮了模態數據中包含噪聲和不完備西安建築科技大學博士論文測量信息的情況。 -
After the orderly reduction methods of massive scattered data being studied, this paper proposes a partial tangent plane slicing method and a virtual second - scan line method after giving a new k - nearest algorithm to re - organize the massive data. the new proposed methods of data reduction and grey theory based unusual noisy data process can be used to generate the scan line type data and it can be directly used to reconstruct curves and surfaces. the research lays a good foundation for reconstructing the cad model in a point - line - surface manner
4 .深入研究了海量散亂數據的有序簡化技術，在提出建立海量數據點鄰接關系k -鄰近新的演算法基礎上，提出了局部切平面切片法和虛擬二次掃描線法，實現了海量數據的有序重組，通過基於灰理論的數據簡化和異常點處理新方法，生成可用於直接重構曲線曲面的掃描線類型數據，為以點?線?面方式重構cad模型打下了良好的基礎。 -
Then the thesis further analyses some core techniques including the system of database, data warehouse and data mining and so on, and presents the frame of function of bank crm. the thesis puts its emphasis on the research on the data preprocessing of data warehouse, data copying, data cleansing, data integration and quality verifying included. finally the thesis discusses the key technology of data warehouse in bank crm - the cleansing of data of customers, and presents some methods of cleansing aiming at noisy values, missing values, conflicting values and duplicated values
本文在充分分析銀行crm的需求的基礎上，提出了基於數據倉庫的銀行crm系統的體系結構，並進一步分析了該體系結構中客戶數據庫系統、數據倉庫、數據挖掘等核心技術組件的內涵，給出了銀行crm系統的功能構架；重點研究了銀行業務系統多年積累的客戶數據向數據倉庫遷移的預處理方法和過程，其過程包括數據復制、數據清洗轉換、數據集成、質量檢驗和數據裝載；最後討論了銀行crm系統應用數據倉庫的關鍵技術：客戶數據清洗，給出了針對噪聲數據、空缺數據、不一致數據和重復數據的清洗方法。 -
Data mining ( dm ) aims at drawing implied and useful information / knowledge from massive incomplete, noisy, blurry, and stochastic real data ; while neural network is a frequently used tool for dm
數據挖掘就是從大量不完全的、有噪聲的、模糊的、隨機的實際數據中發現隱含的、事先未知的潛在有用的並且最終可理解的信息和知識的過程。 -
Data mining means the process of nontrivial extraction of implicit, previous unknown and potentially useful information and knowledge from the large amount, incomplete, noisy, fuzzy and random data
數據挖掘，指的是從大量的、不完全的、有噪聲的、模糊的、隨機的數據中，提取隱含在其中的、人們事先不知道的、但又是潛在有用的信息和知識的過程。 -
Data mining is the process of extracting hidden, unknown but potential useful information and knowledge from vast, incomplete, noisy, fuzzy and random datum. data mining technology is oriented to application
數據挖掘是從大量的、不完全的、有噪聲的、模糊的、隨機的數據中，提取隱含在其中的、事先不為人知的、但又是潛在有用的信息和知識的過程。 -
The fine structures of solar radio dynamic spectra involved in this paper can offer metric information, but we can only get noisy data showing reticulate superimposed on the dynamic spectra
太陽射電爆發精細結構包含許多有用的信息，但是實際中我們只能得到噪聲疊加的太陽射電爆發頻譜圖，表現為頻譜圖中的縱橫條紋。 -
Rough sets theory is a new mathematical tool, which analyses the facts hiding in data without any additional knowledge about the data, and a pithily tool for processing vague, noisy and uncertain knowledge
粗糙集理論是一種新型的處理含糊和不確定性知識的數學工具，它能夠分析隱藏在數據中的事實而不需要關于數據的任何附加知識，是處理含有噪聲、不精確、不完整數據的有力工具。 -
Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data, which is deemed to one of a foreland of data mining system and a promising cross - subject
數據挖掘（ datamining ）就是從大量的、不完全的、有噪聲的、模糊的、隨機的數據中，提取隱含在其中的、人們事先不知道的、但又是潛在有用的信息和知識的過程。 -
Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data, which is deemed to one of a foreland of data mining system and a promising cross - subject. cluster analysis is one of the most important research domains of data mining
數據挖掘是從大量的、不完全的、有噪聲的、模糊的、隨機的數據中，提取隱含在其中的、人們事先不知道的、但又是潛在有用的信息和知識的過程，被信息產業界認為是數據庫系統最重要的前沿之一，是信息產業最有前途的交叉學科。 -
It is observed experimentally and algorithmically that when training data are noisy and overlapping, many support vectors have lagrange multipliers on the upper bound. if it were known beforehand which examples are bound support vectors, these examples could be removed from the training set and their values are fixed at the upper bound. due to the reduced free variable counts, this method is promising to improve training time
實驗和演算法推導顯示在強噪聲和類間重疊數據下訓練svm得到的支持向量很多處于邊界位置，如果我們能夠預先知道哪些樣本是邊界支持向量，這些邊界支持向量的值就可以被固定在邊界處，從而不參加訓練過程，這樣，訓練過程中要優化的變量就可以減少，運行時間也可以縮短。 -
First of all, the emd - based wavelet threshold denoising algorithm is apllied to denoise noisy structural response data to reduce the effect resulting from noise. during the process of the empirical mode decomposition ( emd ), the two boundaries of the response signal are processed with semi - periodical ? semi - symmetrical method. subsequently, hilbert - huang transform ( hht ) is used in identifying structural intrinsic frequency
這套技術用以解決實際工程應用中遇到的在信噪比較低情況下通過結構的響應信號來進行結構損傷識別問題，即先用基於emd的小波閾值去噪演算法對含噪結構響應進行去噪處理，以有效降低噪聲影響（在去噪的emd處理過程中，對信號的邊界採用「半周期半對稱」延括演算法來抑制邊界誤差） ，然後再用希爾伯特?黃變換（ hht ）進行結構的固有頻率識別，最後計算出結構剛度。 -
In the end of each algorithm, we give numerical examples to show how our models can be used to smooth noisy data as well as examples of fusing multiscale data
在每一種方法的最後，我們通過若干模擬實例進一步闡明了多尺度數據融合演算法的進行過程，同時說明了演算法的有效性 -
It is shown that ripper is superior to other algorithms in terms of complexity in computation, classified precision and noisy data adaptability because of its adoption of the repeated incremental reduction mechanism, and it is more suitable to the intrusion detection
由於ripper分類演算法採用了重復增量裁減機制，所以在計算復雜性、分類精度、噪音數據適應性等方面都優于其它分類演算法，更適用於入侵檢測建模使用。 -
But in many cases, the traditional algorithms are hard to apply, or the application effects are not good. these cases include noisy data, redundant information, incomplete data, and sparse data in database. neural networks can acquire knowledge by training set
本文針對現有數據挖掘方法在很多情況下難以推廣應用，例如對于有噪聲的數據、有冗餘的信息、數據不完整、數據稀疏等情況下，這些傳統演算法的使用效果往往不佳。 -
An information entropy - based uncertainty measure is presented first based on generalized rough set model in this paper, which is suitable for evaluating rules retrieved from noisy data. second, this paper puts forward generalized minimal - and - maximal - rules - learning methods and generalized maximal - minimal - rules - conversion model because we can encounter noisy problems in most real - life problems. third, this paper puts forward a new discretization method for the continuous attributes, which is based on the clustering and rough sets theory
本文在對粗集及其相關理論的研究基礎上，首先給出了一種基於推廣粗集模型和信息熵的規則不確定性量度，該不確定性量度適于評價從有噪音數據中提取的規則；鑒于實際應用中經常能遇到噪音的問題，本文提出廣義極小極大規則學習方法，同時還提出了廣義極大極小規則轉換模型gmm ；最後，本文基於聚類方法、結合粗集理論提出了一種新的連續屬性離散化方法。 -
Secondly, a database - populating mechanism is built, along with some object - manipulating operations needed for flexible database design, to support data extraction from huge text stream. thirdly, top - down and bottom - up strategies are combined to design a new extraction algorithm that can extract data from data sources with optional, unordered, nested, and or noisy components
本文採用關系數據庫來組裝抽取對象，給出了將任何ere de -樹映射成關系數據庫模式的演算法，這一數據庫映射機制不僅能夠支持海量文本流的數據抽取，還支持合併鍵定義等抽取對象控制操作，可實現靈活的數據庫模式設計和數據抽取。 -
As for massive, noisy and volatile data, information fusion is the key technology
針對海量報警信號的處理，信息融合技術是關鍵。 -
In this paper, by combining the multiscale representations of signals with data fusion techniques, we describe several algorithms for modeling stochastic phenomena at multiple scales and for their efficient estimation or reconstruction given partial and / or noisy measurements which may also be at several scales
本文通過將多尺度信號表示和數據融合技術相結合，針對不同尺度上擁有不同特徵的多傳感器對同一隨機現象（目標狀態）進行觀測的動態系統，在不同尺度上建立起多尺度隨機動態模型，獲得了一些有效的狀態估計和重構演算法 -
The result shows that the efficiency and precision of prediction for clean data are satisfactory. although there are some errors in the prediction of noisy and chaotic data, the result is acceptable
實驗結果表明，當數據無噪聲時，預測效率與精度都非常高；在處理帶噪聲，並呈現混沌特性的數據時，雖有一定的誤差，但預測結果還是在可以接受的范圍內。
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