pattern classification 中文意思是什麼

pattern classification 解釋
模式分類 將物體賦予模式類的過程
  • 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...
  • 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. Pattern classification that is basic activity of people in living, working and studying is indispensable

    模式識別是人們日常生活、工作、學習中的基本活動，須臾不可或缺。
  2. Secondly, aiming at the necessity of pixel - based tongue color classification system, this dissertation proposes a 2 - stage fcm algorithm. it applies the techniques of pattern classification and makes the better automation of tongue color model construction true

    其次，針對基於像素的舌顏色分類系統的需要，本文提出了一種「二次fcm演算法」 ，採用半監督學習的方式，較為自動化地解決了舌色苔色分佈模型的建立問題。
  3. Application domains include : transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply - chain management, financial engineering, and telecommunications systems planning

    應用領域有：交通和物流規劃、模式識別、圖像處理，資料探勘（資料采礦） 、結構設計，大系統排程、供應鏈管理、金融工程和電信系統規劃。
  4. Due to the unique, stable and live physiological properties of the iris and non - invasive to users, most stable and reliable for identification in practical applications of iris - based system, iris recognition is receiving extensive attention and becoming an active topic in biometrics. as an application - oriented research project, iris recognition integrates mathematic, computer science, optics, electronics and physiology etc. based on wavelet transform, aimed to improve the recognition performance, centered at wavelet - based iris feature representation and pattern classification, we review and explore the iris sequence image quality assessment, iris image pre - processing, iris recognition performance evaluation and several other linked topics. we mainly investigate on the principles and application methodology of wavelet transform for iris feature representation and iris pattern classification methods

    以小波變換技術為基礎，結合圖像處理和模式識別方法，設計並開發了虹膜圖像採集裝置，建立了虹膜識別演算法測試實驗平臺;重點研究了虹膜識別中的小波變換的應用基礎理論與關鍵實現技術;提出了基於小波局部模極大值的虹膜特徵表示及其多重匹配識別、基於小波多尺度信息的一維和二維虹膜紋理特徵量化表示、基於小波過零點技術的虹膜特徵表示及其規范化的部分hausdorff距離匹配識別，這三類方法能夠有效地提取虹膜特徵;基於自建的演算法測試平臺，對上述三類方法和其他三種國內外比較有影響的基於小波變換的虹膜識別方法進行了定量的性能比較和評價，通過實驗數據分析得到了有意義的結論;最後指出了小波變換技術在虹膜識別領域的研究重點與發展方向。
  5. Study of surface emg pattern classification based on bayes decision technique

    決策理論的表面肌電信號模式分類的研究
  6. 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。
  7. 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。
  8. Artificial neural network is a system composed of many computational elements that are connected through an appropriate set of weights for generating complex decision surface. so it can be used in pattern classification

    人工神經網路是由許多以一定權值相互連接的計算單元組成的系統，它可以生成復雜的判決邊界，所以適用於信號分類。
  9. Simulating living being ' s olfactory system, it improves the speed and correctness of pattern classification significantly using nonlinear dynamics method. based on freeman ' s previous work, ko ~ kiii models are analyzed in this paper

    它採用非線性動力學的方法對生物的嗅覺系統進行模擬和建模，模擬嗅覺系統信息處理過程，為信息處理、人工智慧和生物模擬提供了一套全新的思路和演算法。
  10. To solve some existed problems in data mining, the thesis gives out a few resolutions with the new mathematical tool. information theory and multiple statistics are introduced into rough analysis together with rough set theory and other techniques, new results are giving for knowledge discovering, associative rules mining, pattern classification and data cleaning, etc. after a brief summary on data mining and rough set theory, the research works in the thesis can be descript as follows : 1

    Rough集理論是一種新型的處理不確定性知識的數學工具，圍繞著數據挖掘領域存在的問題，本文利用rough集理論與rough分析工具，提出若干解決方案，同時在具體處理問題過程中引入了信息理論、因子分析等方法，與rough分析結合使用，討論了rough集技術在知識發現、關聯規則挖掘、模式分類以及數據清洗等問題中的應用。
  11. High - frequency information after wavelet transform is a kind of one - time feature which is not convenient for pattern classification

    圖像小波變換后的高頻信息屬于小波變換的一次特徵。
  12. In this dissertation, the problems of pattern classification of mechanical products system is researched based on similarity measures

    本課題對基於相似度量的機械產品系統模式分類的相關問題進行了研究。
  13. In this paper we look into the application of moment function and neural network in feature extraction and pattern classification of image recognition respectively

    本文主要研究了矩函數和神經網路分別在圖像識別的特徵提取和模式分類方面的應用。
  14. Analyze the relations among pattern classification, svm and macroeconomic early warning. points out that early warning can be viewed as a process of pattern classification

    分析模式分類、 svm和宏觀經濟預警的內在聯系，指出經濟預警可以看作一個模式分類過程。
  15. Part ii describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques

    第二部分講述語音識別系統中的有關演算法，包括模式分類，查找演算法，隨機模型，語言模型等技術。
  16. 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。
  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 thesis, we propose an efficient nmfs + rbf aggregate framework for fr, in which non - negative matrix factorization with sparseness constraints ( nmfs ) is firstly applied to learn either the holistic representations or the parts - based ones by constraining the sparseness of the basis images, and then the rbf classifier is adopted for pattern classification

    本文提出了一種基於非負矩陣稀疏分解（ non - negativematrixfactorizationwithsparsenessconstraints ， nmfs ）和rbf神經網路的人臉識別方法。通過控制稀疏度， nmfs演算法既可提取人臉全局也能提取局部特徵，再運用rbf神經網路進行模式分類。
  19. Among them kiii model is a high - dimensional chaotic neural network, which cannot only simulate the eeg waveform observed in experiments, but also the biological intelligence for pattern classification

    在k系列模型中， k模型是一個高維的混沌神經網路模型， k網路不僅可以模擬電生理實驗中得到的嗅覺系統的eeg數據，而且也具有模式識別的能力。
  20. In this thesis, several issues concerning the machine learning and the classification of high dimensional multispectral data with limited training samples are addressed, which are based on statistic learning theory ( slt ), support vector machine ( svm ) and artificial neural networks ( ann ). the mai n work and results are outlined as follows : 1. the characteristics of high dimensional multispectral data are studied, and the difficulties that deteriorate the performance of the traditional pattern classification algorithms are carefully analyzed

    以統計學習理論（ statisticlearningtheory ? slt ） 、支持向量機（ supportvectormachine ? svm ）和人工神經網路（ artificialneuralnetworks ? ann ）為基礎，本文開展了以下幾個方面的研究工作：深入分析了高維多光譜數據的特點和傳統模式分類方法在高維多光譜數據分類中面臨的困難。
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