nearest-neighbor algorithm 中文意思是什麼
nearest-neighbor algorithm
解釋
最小距離演算法-
Firstly, based on conventional vq, a fast algorithm named equal - sum block - extending nearest neighbor search ( ebnns ) is presented, which not only can achieve the reconstructed image of full search algorithm but also can greatly reduce both the codeword search ratio and chip area. in order to improve coding efficiency, a new algorithm called correlation - inheritance coding is proposed, which is embedded in conventional vq system to improve compression ratio by re - encoding the indexes
首先，在普通矢量量化基礎上提出了等和值塊擴展最近鄰快速碼字搜索演算法（ ebnns ） ，該演算法在圖像畫質達到窮盡搜索演算法的前提下，大大降低了碼字搜索率和硬體實現面積；為了提高編碼效率，在相關性編碼方面，提出了相關繼承編碼演算法，對普通矢量量化后的編碼索引進行無損重編碼。 -
In the second layer, k - nearest neighbor algorithm is introduced to ascertain searching scope firstly, and then the nerve cell function ' s parameter in hidden layers begin to be evolved in this scope. the least - square is also introduced to calculate connection power between hidden layer and output layer
其中在第二級演化中，先用最小鄰聚法確定搜索空間，然後再在此空廣西大學頎十論文i 13f神經網路在ect圖像重注中的應用研穴間內進行演化，其中用最小二乘法來確定從隱層到輸出層的連接權值。 -
In chapter five to reconst ruct the three - dimensional object cubes, various deconvolution algorithms : nearest neighbor, inverse filtering and constrained iterative deconvolution are developed and applied to both computer generated and experimentally measured image cubes. the best results are obtained using an svd inverse fourier deconvolution algorithm with regularization for noise suppression
第五章為了重建三維目標立方，發展了各種去卷積演算法：最近鄰、逆濾波和帶約束的迭代去卷積，並應用到計算機產生和試驗測量的圖像立方中，最好的結果是利用具有規則抑制噪聲的svd逆傅立葉變換去卷積演算法獲得的。 -
The results are compared with the genetic algorithm in combination with the k - nearest neighbor ( knn ) classification rule
最後，將比較的結果再與基因演演算法結合k個最近鄰法進行比較。 -
Finally we analyze common data association algorithm such as nearest neighbor algorithm 、 probability data association algorithm and joint probability data association algorithm and adopt the nearest neighbor algorithm in our simulation system
然後對當前比較常用的數據關聯演算法最近鄰法、概率數據關聯（ pda ）演算法以及聯合概率數據（ jpda ）演算法進行了分析選擇。 -
In present methods of track - to - tack correlation, the false and lost track - to - track correlation have not been taken into account in the complex background with dense targets. so two kinds of methods are proposed to deal with this problem, one of which is the correlation algorithm based on fuzzy synthetic decision and d - s evidence theory, another is based on k - nearest neighbor ( k - nn ) principle and d - s evidence theory. the two methods combine the logic of fuzzy decision and strictness of statistical classification with intelligence of evidential theory successfully
目前的航跡關聯方法在密集目標環境下，航跡錯關聯概率和漏關聯概率較大，針對這一問題，本文利用d - s證據理論，提出了基於模糊綜合決策的d - s航跡關聯方法和基於k近領域的d - s航跡關聯方法，兩種方法成功地將模糊決策的邏輯性和統計模式分類的嚴密性與證據理論的智能特性相結合，模擬結果說明了兩種方法的有效性和實用性。 -
An optimal projection and dynamic threshold based nearest neighbor search algorithm
基於遺傳演算法的模糊多閾值圖像分割方法 -
The new improved nearest neighbor search algorithm presented based on key dimension, which could be used to improve the performance of compute efficiency
為降低計算時間，提出一種基於關鍵維的近鄰搜索演算法。 -
A method based on fuzzy equivalence relation is applied to implement target classification and a synthetic algorithm is presented to fulfill multi - layer structure among groups by using the nearest - neighbor method and field knowledge
應用基於模糊等價關系的方法實現目標編群，並提出一種基於域知識和最近鄰法相結合的演算法來實現群結構遞增形成的策略。 -
The simulated results indicate that the improved nearest neighbor algorithm has better approaching ability and can obtain better network performance. third, this idarbf neural network is applied to piezoresistance sensor
通過編碼實現，計算機模擬，表明改進的動態鄰聚類演算法較之原演算法有更好的逼近能力，且能獲得較好網路性能。 -
On the other side, we use nearest neighbor approximation to calculate gussian mixture densities, which can reduce recognition time by 6. 67 % compared with standard viterbi beam search algorithm
另一方面，使用高斯混合概率密度的最近鄰快速估算方法，使標準viterbibeam搜索演算法的搜索速度提高了6 . 67 。 -
Further more, we improve the nearest neighbor approximation method by calculat e mixtures ordered by likelihood of being the best scoring mixture. the likelihood is calculating from previously processed data. this improved method can reduce recognition time by 15. 56 % compared with standard viterbi beam search algorithm
本文對最近鄰快速估算方法進行改進，在搜索過程中根據已處理過的數據統計出各個高斯混合分量產生最高對數概率的概率，並依此預測隨后的計算中最有可能產生最高對數概率的高斯混合分量，優先計算更有可能產生最高對數概率的高斯混合分量，使標準viterbibeam搜索演算法的搜索速度提高了15 . 56 。 -
In this density - based outlier mining algorithm, it takes two divided methods to get k - nearest neighbor, which efficiently reduces time complexity and space complexity
基於密度的離群點挖掘演算法對計算數據的k -最近鄰採用二分法，較大減小了時間復雜度和空間復雜度。 -
Improving performance of decision trees with multi - edit - nearest - neighbor algorithm
採用重復剪輯近鄰法提高決策樹演算法的性能 -
These technologies include the procedure of classification and the preprocessing of classification data and compared and evaluated criterion of classification methods. several of typical classification algorithms are compared which are decision - tree and k - nearest neighbor and neural network algorithm. then the emphasis of the paper is induced that divide the classification to eager and lazy and the research of classification algorithm in data mining is based on this divide
討論了數據挖掘中分類的基本技術，包括數據分類的過程，分類數據所需的數據預處理技術，以及分類方法的比較和評估標準；比較了幾種典型的分類演算法，包括決策樹、 k -最近鄰分類、神經網路演算法；接著，引出本文的研究重點，即將分類演算法劃分為急切分類和懶散分類，並基於這種劃分展開對數據挖掘分類演算法的研究。 -
Simulation and practical data test show that better performance can be obtained by this preprocessing. in chapter 3 nearest neighbor data association ( nnda ) algorithm and joint probability data association ( jpda ) algorithm are firstly studied
模擬實驗和對實測數據的處理表明，經過觀測預處理后的觀測數據進行單傳感器濾波將具有更好的估計效果。 -
The optimized feature set feeds a 3 - class classification module, which is based on the traditional binary svm classifier. and the proposed linear programming svm reduces the burden of the svm classifier and improves its learning speed and classification accuracy. a new algorithm that combined svm with k nearest neighbor ( knn ) is presented and it comes into being a new classifier, which can not only improve the accuracy compared to sole svm, but also better solve the problem of selecting the parameter of kernel function for svm
在研究了數據挖掘、支持向量機及其有關技術的基礎上，建立了實現三類水中目標識別的svm方法；採用線性規劃svm解決了傳統二次規劃svm在海量樣本情況下導致的時間和空間復雜度問題；提出了將最近鄰分類與支持向量機分類相結合的svm - knn分類器應用於水中目標識別的思想，較好地解決了應用支持向量機分類時核函數參數的選擇問題，取得了更高的分類準確率。 -
In this paper, we describe the study background, meaning and methods of passive acoustic detective network, summarize the basic theories and methods of target tracking and data association, analyze some tipical data association algorithms include the nearest neighbor algorithm ( nn ), probabilistic data association filtering ( pdaf ), joint probabilistic data association filtering ( jpdaf ), multiple hypothesis tracking ( mht ), and multidimensional s - d assignment algorithm. 2. in detective network, sometimes a surveillance region have only single sensor
從整體上描述了無源聲音探測網路的研究背景、意義、基本框架和研究方法，概述了目標跟蹤與數據關聯的基本理論與方法，重點分析了幾種典型的數據關聯方法，包括最近鄰方法、概率數據關聯濾波器（ pdaf ） 、聯合概率數據關聯濾波器（ jpdaf ） 、多假設跟蹤（ mht ）以及多維s - d分配演算法。
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