algorithm classification 中文意思是什麼

algorithm classification 解釋
演算法分類
  • algorithm : n. 【數學】演算法；規則系統；演段。
  • classification : n 1 選別；分等，分級；分選。2 【動、植】分類（法）。 〈分類級別為： phylum 【動物；動物學】及 div...
  1. Support vector aam based iterative learning algorithm for gender classification

    迭代學習的性別分類演算法
  2. In algorithms, classification algorithms are divided into two cases : one for known statistical distribution model and the other for unknown statistical distribution model. four classification algorithms, the bata - prime statistic model fusing quadratic gamma classifier, based on sar image rcs reconstruction and space position mode, on the mixed double hint layers rbfn ( mdhrbfn ) model and on the self - adapt fuzzy rbfn ( afrbfn ) model, are derived. the problems, including how to further improving the class ratio of the bayes decision, decreasing the dependence on the statistical model and directly providing the adapted algorithm with samples, are solved

    提出了基於徑向基函數神經網路（ rbfn ）的雙隱層混合網路（ mdhrbfn ）模型，解決了標準神經網路在具體sar圖像地物分類中分類類別數目不夠和分類精度差的問題；提出了基於模糊推理系統的自適應模糊rbfn分類（ afrbfn ）模型，兼顧通用性與精確性，增強人機交互能力，進一步提高了演算法分類率。
  3. The document image segmentation is very useful for printing, faxing and such data processing. in this paper, an algorithm is developed for segmenting and classifying document image. feature used for classification is based on the histogram distribution pattern of different image classes

    通過對文檔圖像中不同數據類型直方圖差異的研究，首次提出了一種利用小波域子圖像來增強原始文檔圖像，從而對文檔圖像進行有效分割的演算法。
  4. In this paper, we made an investigation into texture feature extraction and classification based on statistic method and its application in multi - spectral image classification. the research works of this paper have been done as follows : firstly, in order to overcome the weakness of gray level co - occurrence matrix ( glcm ), a new unsupervised texture segment algorithm, based on multi - resolution model, is presented in this thesis

    本文主要研究了基於紋理統計特性的特徵提取與分割方法，並將其用於實際的多光譜圖像分類，具體工作如下:第一，針對傳統灰度共現陣方法中特徵提取的尺度單一問題，本文提出了一種多分辨無監督紋理分割演算法。
  5. One fault diagnosis model and corresponding algorithm was constructed based on neural network and evidence theory for taking a step forward diagnosis correct rate, which can cut down the imput dimension of neural network 、 improve classification ability 、 decrease the error classify rate of diagnosis system. then, the feasibility and effectiveness of this method was manifested by specific diagnosis example

    為進一步提高診斷準確率，本文基於神經網路和證據理論，構建了基於決策層信息融合的故障診斷模型及其相應演算法，目的在於降低神經網路的輸入空間，提高其分類能力，降低診斷系統的誤分類，診斷實例表明了這種方法的可行性和有效性。
  6. The disquisition includes choice of algorithm, accomplish of algorithm, collection of learning sample, parameter of net, shortcoming of bp algorithm, extraction and reduction form line etc. referring to shortcoming of traditional bp algorithm, a modified learning factor with adaptation is introduced. because of every different font has robust, the way based chain coded and knaggy feature is used. a bizarre sample feature database is constructed for speeding up modified bp learning and classification

    本文對人工神經網路理論進行了研究，探討了網路形式及演算法的選擇、演算法的實現、學習樣本的收集、網路參數選擇、 bp演算法缺陷、表格線提取、還原、生成及字元識別、還原生成等問題，並針對bp演算法的缺陷提出了和實現了改進型bp演算法，使網路學習效率提高，對不同人的不同字型字體有較強的魯棒性，採用了基於鏈碼特徵和凹凸分佈特徵的方法來抽取字元特徵。
  7. Some open testing datasets and real gene microarray data are applied in experiments. the results have verified the feasibility and validity of the pica to get the gene microarray data and the advanced boosting algorithm for gene classification

    經過對公開的測試數據集和真實的基因微陣列數據大量實驗，證明了用部分獨立分量分析方法獲取基因微陣列數據及boosting改進演算法進行基因模式分類的可行性和有效性，最終也完成了基因模式的識別任務。
  8. Rough set classification algorithm based on pl sql

    的粗糙集分類演算法研究
  9. Firstly, the complex characteristics of the seal images caused in the process of producing conditions are analyzed. to solve these problems respectively, the circularity clusters and the ostu method are firstly used to realize the shape classification and threshold processing of different seal images. then the image denoise is performed well by scanning beam seed filling and labeling algorithm

    論文中首先分析了印鑒圖像由於蓋印條件造成的圖像本身的一些復雜特點，提出了運用圓形度聚類和最大方差比演算法對圖像進行形狀分類和閾值處理，隨后利用掃描線種子填充演算法和貼刪標簽演算法進行噪聲的去除等預處理。
  10. In the implementation of data classifier, we describe extraction and management of conceptual hierarchy for data, also design an automatic extraction algorithm for numeric data. in this section, we still provide the two algorithms of concept - based attribute - oriented induction and evaluating classification scheme and the visualization of classification rule. finally, the data classifier is tested in databas the results show that it is practical and its performance meet the requirement of designing

    然後，在數據分類器的實現中，論述了數據的概念層次提取和管理，並對數值型數據給出了一個自動提取概念層次演算法；同時給出了基於面向屬性歸納的分類演算法、分類模式的評價演算法和分類規則的可視化方法。
  11. Based on the clustering property of the basis function of sparse coding, a basis function initialization method using fuzzy c mean algorithm is proposed to help the energy function of sparse coding to converge to a better local minimum for recognition. experimental results show that the classification and the sparseness of the features are both improved

    經過模糊c均值聚類初始化后的基函數能夠讓稀疏編碼的能量函數收斂到一個更有利於識別的局部最小點，試驗結果表明特徵的分類性和稀疏性都得到了提高。
  12. The compare is made between the one - versus - one, one - versus - rest and directed acyclic graph algorithm of presented support vector machine multi - class classification algorithms. the simulations show the advantage of directed acyclic graph algorithm over the others the identification rate

    分析比較了現有支持向量機多值分類演算法中的一對一、一對多和有向無環圖演算法，分析表明有向無環圖相對于其它兩種演算法，不僅速度快，而且識別率也較高。
  13. A double - bagging machine - learning algorithm was used to train classification rules on the basis of a combination of fdt scores and nerve fiber related visual field losses in swap

    在聯合fdt評分和swap神經纖維相關的視野缺損基礎上用雙相機器學習系統排列分類法則。
  14. A systematic summary of previous work has been given first. then this paper presents a novel multi - stage face detection algorithm, which makes a good use of human face pattern ' s valuable information in colour image sequences. the difficult detection task has been divided into four steps : the preprocessing, which is to gain skin colored regions with human skin color model ; the roughly detection and face region refining by elliptic curve fitting ; the fine detection with facial features " detection and location ; the face / non - face classification step based on pca and gaussian density estimation technique

    本文對彩色序列圖像中的人臉檢測和跟蹤技術進行了深入的研究，其具體內容為：對近年來的研究工作進行了系統的介紹；提出了一個由粗到細的多階段的人臉檢測演算法，該演算法充分利用了序列圖像中人臉模式的各種有用信息，將復雜的檢測工作分為了四個部分：膚色區域分割預處理，人臉粗檢及利用橢圓擬和的人臉區域提煉，應用人臉基本特徵檢測和定位的人臉細檢， pca結合高斯概率密度估計的人臉驗證。
  15. 3 ) k - mean clustering algorithm is used classification. the category is two, according to the object and the experience knowledge

    3 ）應用無監督分類演算法中k均值（ k - means ）聚類演算法對輸入特徵向量進行分類。
  16. A fingerprint verification algorithm based on fingerprint classification

    一種基於指紋分類的指紋識別演算法
  17. We first realize the classical fingerprint classification algorithm based on directional field and analyze the experiment data

    我們首先研究和實現了目前較為常用的基於方向場計算的指紋分類演算法，並結合實際指紋給出了實驗及其分析。
  18. An automatic fingerprint classification algorithm classifies a fingerprint into a number of pre - specified categories according to the features extracted from the fingerprint

    指紋分類演算法將輸入指紋歸入到預先規定好的若干大類中。
  19. In the end of this paper, target detection algorithm, classification algorithm and decision fusion algorithms are combined to label the state of the targets

    最後，本文在多波段sar目標檢測結果融合的基礎上，結合高波段sar圖像分類結果，實現了對目標狀態的標注。
  20. There are two different visualization approaches of 3d - data sets, one is surface rendering algorithm, the other is volume rendering algorithm. the latter is the emphasis of the paper. the paper describes its optical modek algorithm classification and discusses its future applications and problems to be solved

    體繪制演算法是本文的研究重點，本文介紹了體繪制演算法的光照模型、演算法分類和發展方向，並以光線投射演算法為例，詳細的論述了體繪制演算法的原理、流程、關鍵技術。
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