pattern classifier 中文意思是什麼

pattern classifier 解釋
模式分類器
  • pattern : n 1 模範，榜樣；典範。2 型，模型；模式；雛型；【冶金】原型。3 花樣；式樣；（服裝裁剪的）紙樣；圖...
  • classifier : n. 1. 分類者。 2. 【礦物】分級機。3. 【化學】分粒器。4. （漢語等中的）量詞。
  1. Fuzzy associative memory pattern classifier, fampc

    傳統的參數活動輪廓模型
  2. On the basis of that, the method of acquiring characteristics by infrared plane detection is given, according to the characteristics gained and the pattern recognition, a fuzzy - neural vehicle classifier based on the vehicle ' s length height and axles is carried out

    採用二維紅外檢測技術獲取車輛外形幾何參數的方法，運用模式識別的理論，結合模糊邏輯和神經網路兩者的優點，進行車型自動分類器設計。
  3. Since it is a new pattern recognition method, some fundament knowledge was discussed in this paper, such as the basic notion of subspace method, the construct of subspace classifier, the classing rules, the rejecting rules, etc. at last the representative blsm and alsm classifier were tested with multiple character sets

    作為一種新的模式識別方法，本文講述了子空間方法的基本理論、子空間分類器的構造、分類決策規則、拒識規則等知識，並用多種小字符集測試了具有代表性的blsm和alsm子空間識別器。
  4. A handwritten numeral recognition system based on the combined classifier is built up in this paper. many pattern recognition ways and many handwritten numeral characters are used in the system

    本文建立的基於組合分類器的手寫數字識別系統，綜合使用了多種模式識別方法，全面反映了手寫體數字各方面的特徵。
  5. Measure some feature of one image and take it to classifier, this is an important step of pattern recognition

    把某一圖像的某種特徵進行度量並交給分類器，是模式識別的重要環節。
  6. The output information of single classifier has three forms of abstract, rank and measurement single classifier supplies both the unknown pattern classifying information on the measurement level and the wrong classifying distribution information of the training samples on the abstract level, which are used to design the fuzzy multiple classifiers combination method

    單個分類器的輸出信息有三種表現形式：符號層、排序層、度量層。應用單個分類器在度量層次上，對未知模式的分類信息；在符號層次上，訓練樣本的錯分類分佈狀況，設計了模糊多分類器組合方法。
  7. There are two steps in the combined classifier. the least distance pattern recognition method is used to classify roughly in the first step

    組合分類器的結構如下：第一級分類器採用最小距離法進行粗分類。
  8. Classification has always been a central issue on data mining, machine learning and pattern recognition, classifier, as an important model and method of machine learning and data mining, is very important to the development and application of machine learning and the data mining. the classifier ’ s effect closely correlates with the characteristic of data sets, at present, the construction of classifier is generally based on the character of different datasets, there is no such a classifier which is suitable for any data sets. under uncertain conditions, the bayes network is a powerful tools for the knowledge expression and inference, but for difficulties in constructing its network structure and very high time complexity, it has not been considered as a classifier algorithm until the emergence of na ? ve - bayes classifier

    分類一直是數據挖掘、機器學習和模式識別等研究的核心問題，貝葉斯網路是作為知識表示和推理的強大工具，由於搜索空間巨大和學習困難的原因，直到樸素貝葉斯理論的出現才被作為分類器演算法，改進樸素貝葉斯分類器是貝葉斯分類器學習的一個主要的研究方向。遺傳演算法本質上是一種求解問題的高效并行全局搜索演算法，適合應用於那些改進的分類器的結構學習中。本文提出了一種基於遺傳演算法的ban分類器演算法。
  9. We designed and made the attraction device to attract agricultural pests, obtained agricultural pests " images with the color camera, processed images based on wavelet analysis. on the basis of these, we emphasized on extracting effective features, put forward recognizing pests " classes with pests " colors and texture features, and succeeded in extracting five efficient features such as color features, wave image edge moments features and so on. then we selected features, inputted into neural network classifier, recognized pattern, presented detection results

    本文設計製作了誘捕裝置，誘集農田害蟲，使用攝像頭攝取害蟲圖像，採用小波分析進行圖像處理，在此基礎上重點進行了特徵提取工作，提出了利用害蟲顏色和紋理等特徵進行種類識別的觀點，並成功提取了彩色特徵、小波圖像邊緣矩特徵等五類有效特徵，並經特徵選擇后輸入神經網路分類器進行模式識別，最後給出檢測結果。
  10. The pattern recognition results prove that the kiii network is a good pattern classifier

    由模式識別的實驗結果看出， k網路是一種較好的模式分類方法。
  11. In view of the feature of neural network and its advantages in pattern recognition, we have a great deal work in off - line handwritten character recognition based on neural network. we present two recognition methods based on neural network. one of which is the hybrid neural network recognition system, a multi - level neural network classifier constructed by using the multi neural networks integration technology

    由於神經網路的特點及其在手寫體字元識別領域體現出的潛力，本文對基於神經網路的手寫體字元識別技術進行了大量的研究工作，提出了兩種新穎的基於神經網路的手寫體字元識別模型，其中，基於混合神經網路的手寫體字元識別模型利用了在抗干擾和描述字元拓撲結構方面具有互補性的中心投影特徵和llf特徵，使用多神經網路集成技術構建了多級的神經網路分類器。
  12. The subsystem of car license plate recognition is composed of license plate detection and character recognition. the lpr system involves numerous discipline domains, such as pattern recognition and artificial intelligence, computer vision, digital image processing etc. its key techniques include the license plate detection, the license plate image pretreatment, character segment and the design of classifier

    車輛牌照識別子系統又分為車牌定位、車牌字元識別兩部分，它的研究主要涉及到了模式識別和人工智慧、計算機視覺、數字圖像處理等眾多的學科領域，關鍵在於車牌的定位、車牌圖像的預處理、車牌字元的分割和分類器設計。
  13. The residual signals will be gotten from the comparison between the actual and prediction states. use a som neural networks as a classifier to classify characteristics contained in the residuals. so, we can detect whether the traffic incident has happened. this algorithm can detect not only whether the traffic incident has happened but also the level of accidental congestion caused by the traffic incident the second is another freeway traffic incident detection novel algorithm based on art2 neural networks. this algorithm uses the freeway traffic flow model and art2 neural networks as observer and classifier, respectively. the residual signals will be gotten from the comparison between the actual and estimated value of observer. use the art2 neural networks to classify characteristics contained in the residuals. so. we can detect whether the traffic incident has happened. this algorithm can recognize new pattern at the same ti

    第二種是基於ar咒神經網路的檢測高速公路交通事件的新演算法。該演算法利用高速公路交通流模型和artz神經網路分別作觀測器和分類器，觀測器估計的數據和實際交通數據進行比較，得到殘差序列;利用artz神經網路對殘差序列進行分類，以區分不同交通狀態下的交通信息，達到檢測交通事件的目的。本演算法不但可以識別已知的交通事件類型，還可以識別未知的或從未出現過的交通事件類型，是一個可以邊工作、邊學習的檢測系統。
  14. Data warehouse is a hot research area in 90s its main motif is to provide the decision - maker a powerful tool : gathering the data in pure consistent, relevant pattern, and making use of the data in managing analyzing, data - mining purposec that means that the decision - maker can use the tool to understand, grasp the situation of the business from different directions and forecast the future of it when using data warehouse, the processing speed determines data warehouse ' s practicability and processing ability the hoc ( highway decision center ) system realized before solves some key problems about intermediate scale data, mainly concentrating data warehouse performance coefficient when using hdc in large scale data, it encountered processing speed problem then the settlement of this problem becomes a major research point so, based on the former research achievements, the present task is to construct the renowned data warehouse architecture and its relevant algorithms, then adapts the system to the large scale dataset with data mining functions c this paper is a part of the research in order to construct the powerful system, a key problem is to cope with the processing - speed problem and the data space problem, etc, - caused by the large scale dataset and magnificent dataset this is also the core in the present data mining research this paper ' s motive is to design and realize a decision - tree classifier in the data warehouse system for large - scale dataset

    大型數據倉庫的處理速度問題目前是制約其推廣應用的關鍵所在，也是這一領域的一個重要研究課題，也正是我們當前工作的重點：在前期研究工作的基礎上圍繞提高大型數據倉庫處理速度問題，建立改進的數據倉庫系統模型和相關演算法，開發出面向中級以上企事業單位的、具有數據挖掘和分析能力的大型數據倉庫系統。建立大型數據倉庫所面臨的關鍵問題，是如何妥善解決實際業務數據的大規模、海量特徵所帶來的處理速度和空間等問題，這也是當前挖掘技術研究必然面對的核心問題。本研究的目的是設計並實現大型數據倉庫系統中的分類數據挖掘工具? ?決策樹分類器，主要工作是在綜合了解現有決策樹分類演算法的研究情況的前提下，對決策樹演算法適應大規模數據集的問題進行探討，力求設計出能較好地適應大規模數據的分類器演算法。
  15. But, such a vectorization will bring at least three potential problems : 1 ) structural or local contextual infor mation may be broken down ; 2 ) the higher the dimension of input pattern, the more me mory space are needed for the weight vector related to a classifier ; 3 ) when the dimension of a vector pattern is very high and while the sample size is small, it is easy to be overtrained

    如此轉換至少會帶來三個不足: 1 ）空間或結構信息可能會遭到破壞; 2 ）由於權向量的維數等於輸入模式的維數，當輸入模式維數很大時，權值的存儲空間相應的會很大; 3 ）對于大維數的向量模式，當樣本數不多的時候，利用線性分類器易導致過擬合。
  16. Recognition of heart rate variability signal using fuzzy associative memory pattern classifier

    來識別排序相鄰與不相鄰情況的心率變異性
  17. The wavelet packet transform is investigated as a novel means of extracting time - frequency information and reducing dimensions of feature from vibration signal in the infinite time domain. neural network is discussed as a pattern classifier to finish damage pattern classification by inputting node energy feature vector

    小波包變換以其特有的高頻解析度能夠實現對結構振動信號的特徵提取，可以把時域無限的測試振動信號降維，得到有限個節點能量信號構成的特徵指標。
  18. In this dissertation, base on the review and analysis of current mainstream algorithms and techniques, we build up whole rtr system, and study some efficient recognition methods for different radar characters. the major research work and contributions in this dissertation are summarized as below : 1. summarized current popular pattern recognition methods, this dissertation researched in the algorithms and performances of nearest neighbor ( nn ) classifier, multi - layer perceptron and rbfn ( radial basis function network )

    本文在總結當前主流雷達目標識別演算法的基礎上，建立起基於gbr的雷達目標識別系統，並且對彈道導彈的各種特識別方法進行了研究，主要進行的工作和創新有: 1 .研究和總結了當前常用的分類識別方法，針對雷達目標識別的特點，對近鄰分類器、多層感知器和徑向基函數網路（ rbfn ）分類器的演算法和性能進行了研究。
  19. Furthermore, good compression effectivity is presented in application to compression of pd gray intensity images. according to the research on difference degree between computational values of fractal features extracted from decoded pd images and that from original images, it is shown elementarily that the proposed method is effective for application in pd pattern auto - recognition system. ( 4 ) this paper brings forward pd pattern auto - recognition project based on the above recognition features and fractal compression of pd gray intensity images and designs the classifier with back - propagation neural network ( bpnn )

    （ 3 ）研究了局部放電灰度圖像的四叉樹分形圖像壓縮方法，通過模擬實驗證明採用本文演算法能夠獲得一定的圖像壓縮比，在局部放電灰度圖像壓縮應用中顯示了良好的壓縮效果，進一步研究了局部放電解碼圖像的識別結果與原始圖像之結果的差異程度，研究結果初步表明該方法應用於局部放電模式自動識別系統中是有效的； （ 4 ）研究了基於局部放電解碼圖像的bpnn識別方法及，通過分析對解碼圖像的識別效果，驗證了設計的系統模式識別方案的有效性，同時表明該方案能夠滿足實地局部放電模式自動識別和遠程數據通訊及自動識別的需要。
  20. This paper is concerned with the popular texture analysis technique in the traditional image recognition, the novel pattern recognition approach, namely synergetic neural network pattern recognition, and their applications. furthermore, it proposes and implements an offline handwriting identification multi - classifier. it is organized as follows

    本論文研究了傳統圖像識別方法中常用的紋理分析法及一種新的模式識別方法? ?協同神經網路模式識別法及其應用，並在此基礎上構造了一個離線手寫體筆跡鑒別多分類器模型。
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