例句與造句

1. 15 george karypis , euihong han . fast supervised dimensionality reduction algorithm with applications to document categorization retrieval
   具體做法就是，先采用拉推策略來修正類中心，然后把修正的類中心作為壓縮空間的坐標(biāo)。
2. And there is no need to use every band to do classification and targets identify , so it is necessary to do dimensionality reduction first
   目標(biāo)識別和分類等圖像處理并不一定需要全部的波段來進(jìn)行,因此對高光譜圖像進(jìn)行數(shù)據(jù)降維是十分必要的。
3. Pochet , n . , et al . " systematic benchmarking of microarray data classification : assessing the role of nonlinearity and dimensionality reduction . " bioinformatics , 2004
   基因芯片數(shù)據(jù)分類的系統(tǒng)基準(zhǔn)：評估非線性和維度的縮減。 《生物信息學(xué)》 , 2004年。
4. The work of the paper mainly includes : ( 1 ) present a model for measuring the similarity between two hydrological time series . in this model , we adopt an intuitive dimensionality reduction technique for hydrological time series which is called piecewise average approximation ( paa )
   主要工作包括： （ 1 ）提出了適合水文時間序列數(shù)據(jù)特點的相似性模型，采用簡單直觀的等時間間隔序列分段平均值技術(shù)（ paa ）作為水文時間序列降維方法。
5. This paper deeply studies the manifold learning method called locally linear embedding ( lle ) and improves it . the main achievements in this paper are as follows : 1 . it summarizes the development of manifold learning currently , analyzes the characteristic of nonlinear dimensionality reduction methods , compares the virtues and drawbacks , and makes correlative computer experiments
   本文主要對基于流形學(xué)習(xí)的局部線性嵌入( lle )算法進(jìn)行了深入的研究與改進(jìn),具體工作包括以下四部分: 1 .簡要綜述了當(dāng)前流形學(xué)習(xí)的發(fā)展概況,對現(xiàn)有各種非線性降維方法的特點進(jìn)行分析,比較優(yōu)點和不足,并進(jìn)行了相關(guān)的計算機仿真實驗。
6. It's difficult to find dimensionality reduction in a sentence. 用dimensionality reduction造句挺難的
7. One is the performance of data mining algorithms degrades , the other is many distance - based and density - based algorithms maybe not effective . these problems can be solved by the following methods : l ) transport the data from high dimensional space to lower dimensional space by dimensionality reduction , then process the data as lower dimensional data . 2 ) to improve the performance of mining algorithms , we can design more effective indexing structures , adopt incremental algorithms and parallel algorithms and so on
   解決的方法可以有以下幾種：一個可以通過降維將數(shù)據(jù)從高維降到低維，然后用低維數(shù)據(jù)的處理辦法進(jìn)行處理；對算法效率下降問題可以通過設(shè)計更為有效的索引結(jié)構(gòu)、采用增量算法及并行算法等來提高算法的性能；對失效的問題通過重新定義使其獲得新生。
8. At this time , linear methods fail to find the correlations and distributions of high - dimensional data . to solve this problem , manifold learning approaches have been proposed , which break the traditional linear dimensionality reduction frame mostly constituted by principal component analysis , and gain extensive attention soon
   針對高維數(shù)據(jù)的非線性特性,一些基于流形學(xué)習(xí)機理的非線性降維方法被提出,打破了以主成分分析為主的傳統(tǒng)線性降維方法的框架,很快得到國內(nèi)外的廣泛關(guān)注。
9. Pervious research in pca and flda mainly focuses on vector - based pattern representation methods for feature extraction and dimensionality reduction , i . e . all patterns must be transformed into vector before any subsequent processing . so patterns represented in matrix form ( e . g . an image ) must be stretched into vectors in preprocessing step
   現(xiàn)普遍使用的pca 、 flda方法，是針對向量模式進(jìn)行的特征提取和降維方法，亦即，所有的模式都要進(jìn)行向量化的操作，因此對于矩陣表示的模式(如圖像)就必須首先將其轉(zhuǎn)換成向量。
10. Adopting the globe pole mapping method of space analytic geometry , forming a topological mapping model from the high dimensionality vector to the low one , and then realizing a corresponding mapping from the rectangular matrix high dimensionality space text set to the low dimensionality space text set , finally , composing the corresponding arithmetics , accordingly solving the problem of nonlinear dimensionality reduction for text mining effectively , and overcoming some drawbacks in the former researches
    摘要采用了空間解析幾何中的球極映射方法，形成高維向量到低維向量的拓?fù)渥儞Q模型，實現(xiàn)了矩陣形式的高維空間文本集合到低維空間文本集合的一一映射，編制了相應(yīng)的算法，從而有效地解決了文本挖掘中的非線性降維問題，克服了以往研究中的缺陷。
11. Fast and high - quality document clustering algorithms play an important role towards this goal as they have been shown to provide both an navigation / browsing mechanism by organizing large amounts of information into a small number of meaningful clusters as well as to greatly improve the retrieval performance either via cluster - driven dimensionality reduction or term - weighting
    快速和高質(zhì)量的文本聚類技術(shù)在實現(xiàn)這個目標(biāo)過程中扮演了重要的角色。通過將大量信息組織成少數(shù)有意義的簇，這種技術(shù)能夠提供導(dǎo)航瀏覽機制，或者，通過聚類驅(qū)動的降維或權(quán)值調(diào)整來極大地改善檢索性能。