線性分類器 的英文怎麼說

中文拼音 [xiànxìngfēnlèiqì]
線性分類器 英文
linear classifier
  • 線 : 名詞1 （用絲、棉、金屬等製成的細長的東西） thread; string; wire 2 [數學] （一個點任意移動所構成的...
  • 性 : Ⅰ名詞1 （性格） nature; character; disposition 2 （性能; 性質） property; quality 3 （性別） sex ...
  • 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
  • 線性 : [數學] [物理學] linear; linearity線性代數 linear algebra; 線性方程 linear equation; 線性規劃 line...
  1. Combining methods and diversity measures in multiple classifier systems for thematic classification of landsat tm images

    影像分類領域多分類器線性組合方法及差異性度量研究
  2. Through the theory of light radiation and intensity, we can use the fewest leds to satisfy the luminous intensity demand. through image segmentation theory, we can accurately pick module up from the test stripe when it is put in wrong directions. through image processing theory, we can acquire correct information and avoid the bad effects from the asymmetric chemistry reaction and instability of the devices

    用光的輻射和強度理論，我們計算出了獲得足夠圖像強度所需的最少光源;用圖像分割理論，我們在試紙條傾斜放置或有垂直方向上的偏移時，準確地提取出了各模塊的數據;用平滑濾波和均值濾波理論，我們濾除了由於反應不均勻及硬體設備不穩定帶來的噪聲;用交遇區設計線性分類器的方法，我們降低了有限樣本設計線性分類器帶來的誤差，提高了檢驗準確度。
  3. 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。
  4. 2 ) to increase the difference, the non - linear transform function is used. the each pixel is computed by the average of a window ' s energy, which is gabor wavelets energy and the input feature vector of unsupervised classification

    2 ）對濾波后圖像進行非線性處理，以加大不同類之間特徵的差異，給出了計算圖像gabor小波能量特徵的計算方法，該能量特徵作為無監督分類器的輸入向量。
  5. These three classifiers are a linear classifier based on fuzzy features, a hierarchy classifier based on features of geometry definitions and a distance classifier based on frequency features of stroke curvature

    使用的分類器分別為基於模糊特徵的線性分類器、使用幾何定義特徵的分層分類器以及基於曲度頻域特徵的距離分類器。
  6. Each band of hyperspectral image has the same physical structure, so we classification the first band, and design an optimal linear predictor for each class to make the mean prediction square error minimal, and then we use jpeg - ls algorithm to remove the spatial redundancy

    由於高光譜圖像每個波段都具有相同的物理結構，先對首幅圖像進行分類，在每個子類中分別使用各自的最佳線性預測器，將該類中的相鄰譜段進行預測並將預測殘差均方降為最小，然後用jpeg - ls演算法去除殘差圖像的相關性。
  7. The operating object of all these linear classifiers is vector pattern, i. e., before applying them, any non - vector pattern should be firstly vectorized into a vector pattern

    然而現有的線性分類器幾乎都是針對向量模式的，即所有的模式都採用向量表示，要應用於矩陣表示的模式，必須首先將矩陣模式轉換成向量模式。
  8. In this paper, inspired by the method of feature extraction directly based on matrix patterns and the advantage of mhks, we develop a new mhks classifier based on matrix patterns ( matmhks ). the method can mitigate the above shortcomings. we also make a further try of applying the algorithm proposed above to breast cancer detection

    受到已有面向矩陣的特徵提取方法的啟發，本文將此方法引入到正則化h - k線性分類器的設計中，設計出面向矩陣模式的雙邊正則化h - k分類演算法matmhks ，克服了以上不足，並繼承了mhks演算法的優點。
  9. The total number of subclassifiers in our new method linearly scales with the number of classes and the size of each subclassifier is smaller than that of the correspondent binary svm subclassifier used in the original method

    此種構造方式使子分類器數目僅隨類別數作線性增長，且子問題規模小於前一種方式中任一兩類分類器的規模。
  10. This article, aiming at the specialties of rmb currency image, puts forward a new method using linear transform of image gray to diminish the influence of the background image noises in order to give prominence to edge information of the image. then the edge characteristic information image is obtained by edge detecting using simple statistics. by dividing the edge characteristic information image in the width direction into different areas, getting the number of the edge characteristic points of different areas as input vectors to random masks and optimized by ga

    文中提出了利用圖像灰度線性變換來抑制背景圖案噪聲的影響，突出圖像邊緣信息；然後採用簡單統計法進行邊緣檢測，得到邊緣特徵信息圖；最後通過對邊緣特徵信息圖在寬度方向上進行均勻劃分成不同的區域，統計不同區域的邊緣特徵點的數目作為神經網路的初始輸入向量，對初始輸入向量用隨機掩碼處理和遺傳演算法進行優化得到最終輸入向量，通過三層bp神經網路分類器進行分類，達到了人民幣識別的目的。
  11. 4 different types ’ features were generated, namely ar model parameters, power spectral frequency band intensity, energy for wavelet packet decomposition, wavelet packet entropy. every type of features were extracted respectively using pca and ica method and classified using linear neural network, knn and bp network

    建立了ar模型參數、功率譜估計頻帶強度、小波包分解能量比率、小波包熵四種特徵，分別使用pca與ica進行特徵提取，採用線性神經網路、 k -緊鄰法、 bp神經網路四種分類器進行分類。
  12. Finally, combining the two extraction methods with the two classification methods, the thesis put forward four models of palmprint recognition : k - l + ld model, k - l + nn model, nn + ld model and nn + nn model. the experiments show the accuracy, efficiency and the fault tolerance ability of these models. in terms of their characteristic, we can apply them in various fields

    論文把兩種特徵提取方法和兩種分類器設計方法進行結合，提出k - l變換與最小分類器、 k - l變換與bp神經網路分類器、線性神經網路與最小分類器、線性神經網路與bp神經網路分類器四種組合，最後對四種識別方法進行比較，根據它們識別的準確率、效率以及容錯能力對識別結果進行分析，總結出各種方法的優缺點，根據它們的特點，提出在不同方面的應用。
  13. Based on the formers, this dissertation efficiently selects the face features abstracting using ica. with no decline of recognition rate, the feature dimension is reduced, so the course of recognition is accelerated. support vector machine pattern recognition method is based on vc dimension theory, adopting the srm principle and considering training error and the generalization ability, which has shown many special advantages in dealing with small samples, non - linear and pattern recognition in high dimension

    本文採用基於矩陣s的人臉表示方法，將ica特徵選擇的概念和演算法用於人臉特徵的提取和優化，在不影響識別率的情況下，降低了特徵維數，提高了識別速度；支持向量機（ svm ）模式識別方法基於vc維理論，採用結構風險化原理，兼顧訓練誤差和泛化能力，在解決小樣本、非線性及高維模式識別問題中表現出許多特有的優勢；對于多類問題，介紹並採用了「一對一」的策略進行svm分類器設計；對于圖像預處理，詳細介紹了幾何歸一化的演算法步驟。
  14. 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。
  15. In the project we process the data with the methods of smooth filter, boundary division technique and fisher linear sorting method

    演算法上採用了平滑濾波技術，邊界分割技術和交遇區方法設計fisher線性分類器。
  16. 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。
  17. Experimental results show that these three classifiers can get high recognition rate. they are simple, efficient, nonlinear and suitable for rtr. 2

    模擬結果顯示這三類分類器適合應用於雷達目標的分類識別，且具有很高的識別率，是一類簡單、高效的非線性分類器。
  18. There are many conventional classifiers, which are generally divided into two categories : linear classifiers and nonlinear ones

    分類器一般分為線性分類器和非線性分類器。
  19. Secondly, in the different level of pattern recognition, we realize feature - level fusion based on neural network and score - level fusion based on multi - classifier. thirdly, some opinions about application of multi - biometrics are given and corresponding prototype systems are realized

    二、依據模式識別的不同層次，分別實現了特徵層的整合和分數層的整合，特徵層整合採用了神經網路方法，分數層整合採用了多層線性分類器方法。
  20. Main works based on the newspaper samples is just as the following : ( 1 ) texture block is designed to embody more font characters. ( 2 ) filter orientation is optimized with genetic algorithm to make the angle set subtler. then the multi - channel gabor filter may extract better features

    通過對實際字體樣本的分析，本文主要完成了以下工作： （ 1 ）設計了更能體現字體特徵的紋理圖像塊； （ 2 ）利用遺傳演算法對濾波角度進行了優化選擇，得到了更為精細的角度集合，以此生成的多通道gabor濾波器能夠提取穩定的字體特徵； （ 3 ）字體樣本的分佈具有多峰性質，以動態聚類演算法得到的分段線性分類器更好地分類字體。
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