模式分類器 的英文怎麼說

中文拼音 [móshìfēnlèiqì]
模式分類器 英文
pattern classifier
  • 模 : 模名詞1. （模子） mould; pattern; matrix 2. （姓氏） a surname
  • 式 : 名詞1 （樣式） type; style 2 （格式） pattern; form 3 （儀式; 典禮） ceremony; ritual 4 （自然科...
  • 分 : 分Ⅰ名詞1. （成分） component 2. （職責和權利的限度） what is within one's duty or rights Ⅱ同 「份」Ⅲ動詞[書面語] （料想） judge
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 器 : 名詞1. （器具） implement; utensil; ware 2. （器官） organ 3. （度量; 才能） capacity; talent 4. （姓氏） a surname
  • 模式 : model; mode; pattern; type; schema
  1. Immune evolutionary algorithms are optimal algorithms in essence, therefore, they can be used in some fields, such as cybernation, pattern recognition, optimal design, meshing learning, network security, etc. there are also some examples of these attempts described in this paper

    免疫進化計算在本質上講是一種優化演算法，所以它們可以應用於一些諸如自動控制、故障診斷、模式分類、圖象識別、優化設計、機器學習和網路安全性等廣泛領域，本文在這方面也做了一些初步的嘗試。
  2. The process of feature extraction is to transform the eradiate noise signal to different feature space and extract the feature vectors that reflect the category of the input sample. the extracted features are the input modes to the classifier

    特徵提取的過程是把輸入的船舶輻射噪聲信號變換到不同的特徵空間，提取出反映樣本的類別特性的特徵向量，並把其作為分類器的輸入模式。
  3. Firstly the patterns of the multifingered hands are detailed, eight patterns are defined. the classical bayes method is used in the classification of pre - grasp of multiple fingers based on three patterns which are grasping, holding and pinching. based on the eight pre - grasp patterns, bp neural network is applied in the classification of the pre - grasp of multifingered hands and gets a good effect. the method solves the shortcoming input sample relying on the propobility density and simplified the un - insititution characters extraction. in this paper, support vector machine ( svm ) and binary - tree with clustering is applied in the classification. this method can solve the slow speed and effect with fewness sample in the classification, achieving a good effect. in this papper, we extract the characters of the regulation object with geometry characters and extact the unregulation object with the image analysis

    此法解決了輸入樣本依賴物體的概率密度的特點，簡化了分類特徵提取的不直觀性。本文還採用了支持向量機（ svm ）和聚類二叉樹相結合的方法對機器人手預抓取八類模式進行分類，解決了預抓取模式分類訓練速度過慢以及在分類中樣本數量偏少而影響分類效果的問題，得到了較高的正確率。本文對預抓取幾何形狀規則的物體採用直接提取其幾何特徵，對于預抓取幾何形狀不規則的物體採用圖像分析的方法進行特徵提取。
  4. We analyze the classifying results based on the fuzzy text classifying, think the wrong classifying results can be divided into two styles, and we propose a subordinative degree update algorithm aim at the two instances. combined the nizzy semantic relationship classifying algorithm, we propose the gradual classifier construction algorithm through checkouting and correcting the wrong results constantly with the update formula.

    在模糊文本分類的基礎上，對分類結果進行了分析，將分類錯誤歸結為兩種類型，並針對這兩種情況提出了隸屬度更新演算法，結合模糊語義關聯度的分類演算法提出了運用更新公式不斷對分類結果進行校驗糾錯進而逐漸地構造分類器的演算法。
  5. In the phase of image pretreatment, the main jobs of this system includes dot operation, image swell, positive chiasma transform, edge extraction and edge swell, outline track, etc. because the visual system itself is a neural system, systematizer designed in the paper adopts bp neural network to accomplish computer image identification, the system has some advantages over the traditional one, but with the extensive application of bp neural network, the problems existing in bp neural network come forth increasingly

    在系統軟體設計部分中，首先是對所選零件進行模式識別，包括圖像預處理、特徵提取和分類器設計三個階段，其中在圖像預處理階段本系統主要做的工作有：點運算、圖像增強、正交變換、邊緣提取和邊緣增強、輪廓跟蹤等。由於視覺系統本身就是一個神經系統，故本文所設計的分類器採用bp神經網路，其具有一些傳統技術所沒有的優點。
  6. 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。
  7. One class classification is a machine learning approach different from the traditional pattern recognition approach where two or more class samples are required. however in some real - life cases, we can hardly, even not, get the samples of some classes, or have to pay costly price to obtain the so - needed samples, such as in the case of machinery malfunction. and while in other cases, the sizes of samples among classes are imbalance, such as medical diagnosis

    單類分類器是不同於傳統模式識別的一種機器學習方法，傳統模式識別方法一般需要多個類別的樣本（至少兩個） ，而在有些場合中，幾乎無法獲取多類的樣本，或者獲取其樣本所需花費的代價非常高，比如：機器故障中我們不可能為了去獲得故障樣本而讓機器特意產生故障；又有些場合的類別樣本個數嚴重不平衡，比如醫學上的疾病特徵與非疾病特徵的比例是嚴重不平衡的。
  8. 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。
  9. 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。
  10. This method can reflect local signal feature and well perform in the experiments. we also present an integrated electromyographic signal ( emg ) pattern recognition scheme. the application of an artificial neural network ( ann ) technique together with a feature extraction technique, for the classification of emg signals is described

    利用高階譜技術提取肌電信號的特徵信息，然後利用奇異值或者其它方法對二維特徵矩陣進行優化，將優化之後的一維特徵向量輸入神經網路分類器進行模式識別，這種方法能夠初步識別不同模式的上肢運動。
  11. Classification is to predict the class label of unknown data with supervisor obtained from experiential data, which is a basic problem in pattern recognitionx machine learning and statistics, as well as in data mining

    分類即通過由經驗數據訓練得到的分類器預測未知數據的歸屬，是模式識別、機器學習、統計分析等領域的一個基本問題，也是一種最常見的數據挖掘任務。
  12. 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。
  13. Firstly, the paper, combining the characteristic of synchronous pulse bursts and inhibition with the modified pcnn model, presents a way of finding the foveation points in the images adaptively and effectively, and simulates the human vision system. secondly, pcnn is extended to pcnns, based on the properties of information couple and transmission, an algorithm that is used to fuse images of the same target got by several sensors to an image is presented to simulate the human vision system. thirdly, combining the properties of synchronous pulse bursts, capture, and transmission and competition of waves, the paper presents two ways of classification, one is an algorithm based on the properties of neuron to capture and inhibit to classify the data taking on any complex unlinear distribution robustly, the other is based on the restricted distance and modified of the former to remove the influence of inferior samples in classification ; fin ally, based on the accumulative difference pictures, and the forming and transmission of pcnn wave, selecting and controlling the direction of autowave by connecting the neighbouring neurons selectively, the paper presents a way to simulate the tracks of moving object and detect the moving direction

    首先結合pcnn的同步脈沖發放和側抑制特性，提出了基於改進型pcnn的圖像凹點檢測演算法，該演算法是一種自適應而有效的圖像凹點檢測方法，並且較好地模擬了人類視覺系統；然後，結合信息傳遞和信息耦合特性，將pcnn擴展成pcnns （ pcnn網路群） ，提出了一種基於pcnns的圖像融合演算法，能夠將多個傳感器獲取的同一目標的圖像信息融合到一幅圖像中，有效模擬了人類視覺系統；另外，結合pcnn的同步脈沖發放特性、捕獲特性和波的傳播競爭特性，開拓地將pcnn用於模式分類中，提出了基於耦合神經元點火捕獲抑制特性的分類方法和改進的約束距離下的pcnn分類方法，前者可實現對樣本空間中任意復雜分佈訓練樣本的穩健非線性分類，而後者能夠消除訓練樣本中刺點對分類的影響；最後，結合累積差分圖像思想、 pcnn波的形成與傳播特性，通過各神經元之間連接取向來選擇與控制自動波的流向，將pcnn用於運動視覺分析中的運動軌跡模擬及運動方向檢測。
  14. In the following virtual memory management subsystem design, after analyzing the hardware - software dividing line and cooperation in detail, four key issues of virtual memory manage subsystem design are discussed : the classification of processor operating mode, the partition of virtual space, the access controlling and the design of control coprocessor ( ccop ). a virtual manage subsystem prototype is then presented

    在虛存管理子系統設計的討論中，詳細分析了虛存管理中軟硬體的分工協作，深入研究並解決了虛存管理子系統設計的四個核心問題：處理器工作模式分類、虛地址空間劃分、訪問控制和控制協處理器設計，並在此基礎上給出了一個虛存管理子系統原型。
  15. According to the requirements to pd pattern auto - recognition, this paper studies systematically the basic theories and realizable methods for auto - recognition of pd gray intensity image : ( 1 ) in the requirement of on - line pd monitoring for transformer, several discharge models are designed and the relevant experiment methods projected. with discharge model tests, a lot of discharge sample data is acquired. on the base of systematical research on recognition for pd gray intensity image, this paper puts forward two kinds of fractal features, the 2nd generalized dimensions of original pd images and fractal dimensions of high gray intensity pd images, and then the relevant extraction methods

    針對局部放電模式自動識別的需要，作者系統地研究了局部放電灰度圖像自動識別中的基本理論和實現方法： （ 1 ）根據變壓器局部放電在線監測的要求，設計了放電模型和實驗方法，並通過模型實驗獲得了大量放電樣本數據，為構造局部放電灰度圖像和採用bpnn進行識別作好準備； （ 2 ）研究了局部放電灰度圖像的構造方法以及降維構造32 32灰度和矩陣的方法；在用人工神經網路對局部放電進行模式識別時，分析了bp網路的優缺點，對典型bp網路的結構和學習訓練演算法提出了改進，採用帶有偏差單元的遞歸神經網路作為模式分類器；採用32 32灰度和矩陣進行bpnn識別結果表明這種方法是有效的。
  16. Pattern classification of flaw was carried out with bp neural network and the feature selected

    利用bp神經網路分類器及選擇的特徵值對缺陷進行模式分類。
  17. Pattern classification of flaw is carried out with bp neural network and the feature selected

    利用bp神經網路分類器及選擇的特徵值對缺陷進行了模式分類。
  18. Perceptron, relaxation, mse and ho - kashyap ( hk ) algorithm. hk is not robust to outliers. the modified hk with square approximation of the misclassification errors ( mhks ) tries to avoid this shortcoming and adopts similar principle to the support vector machine to maximize the separation margin

    線性分類器因其簡單、易於分析和實現且容易推廣為非線性分類器的優點而成為模式分類最常用的分類器，並產生了感知器（ perceptron ） 、鬆弛演算法（ relaxation ） 、最小平方誤差（ minimumsquareerror ， mse ）和ho - kashyap （ h - k ）演算法等經典演算法。
  19. Based on the ideas of pattern classification and local decision referring whole information, a method of coupling neural network of multi - sensor information fusion is propos

    從模式分類和「局部控制、全局參與」的思想出發，提出了耦合神經網路的方法來實現多傳感器信息融合的損傷確認、定位及定量。
  20. Fisher kernel is the first try to link the probability models and the discriminative classifiers like svms, and applied in the detection of biological homology

    Fisher核的提出首次實現了將概率模型與支持向量機等判別式分類器相結合，並應用於生物同源性檢測中。
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