訓練分類器 的英文怎麼說

中文拼音 [xùnliànfēnlèiqì]
訓練分類器 英文
training classifier
  • 訓 : Ⅰ動詞1 （教導; 訓誡） lecture; teach; train 2 （解釋） explainⅡ名詞1 （準則） standard; model; ex...
  • 練 : Ⅰ名詞1 （白絹） white silk 2 （姓氏） a surname Ⅱ動詞1 （加工處理生絲） treat soften and whiten s...
  • 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
  • 訓練 : train; drill; manage; practice; breeding
  1. Singularity mainly improve on its application way, and bring forward a new count means of num about pels part - area. fourthly, to recognize target, is to train target character samples imput to interpolator, then test using testing samples to get recognition result

    然後是對目標進行識別，將目標特徵樣本輸入到分類器中進行訓練，再運用測試樣本進行測試，得到了良好的識別結果。
  2. 4. using bp neural network on setting up recognition classifying device of leucocytes images, and then train it

    運用bp神經網路建立白細胞圖像識別分類器，進行網路訓練。
  3. Finally, a typical two classes example of two classes natural spearmint essence was employed to verify the effectiveness of the proposed approach cca - svm. the classification accuracy is much better than that obtained by svm alone or correlative component analysis - self - organizing map ( cca - som ) networks

    然後將其成功地應用在建立留蘭香的分類器模型上，它的訓練與預浙江大學博士學位論文測分類精度比svm方法、分類相關成分分析一自組織映射網（ cca一som ）方法都有明顯提高。
  4. 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。
  5. In tcm this pattern is called pair of medicine, and it can be resolved by frequent pattern mining. the symptom complex diagnose can be treated as a bayesian training and a bayesian classification on large clinical database cases. the critical step to resolve the chinese prescription compounding is to build an appropriate model to express the progress of it

    中藥知識發現集中在發現常用的單味藥合用模式，在中醫術語中稱之為藥對，這可以用高頻集發現來解決；中醫癥候診斷可以看成是在大量臨床案例庫上的貝葉斯訓練器和分類器；解決方劑配伍問題的關鍵是建立起一個合適的配伍計算機模型。
  6. According to the utilized face database, three facial expression categories are defined : neutral, happiness and anger. the categorization architecture is based on a som. in order to eliminate influence of initial values and sequence of input examples in som, supervised learning is introduced into the training stage

    分類器的設計採用的是基於自組織神經網路的方法，為了克服傳統的自組織映射神經網路的訓練結果容易受訓練樣本的輸入順序和權值初值影響，而導致訓練結果不符合期望的問題，因此，在訓練過程中引入了監督機制，以使訓練結果與期望相符。
  7. 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。
  8. ( 5 ) a series of design methods of classifiers are proposed, including the classifier based on the generalized inverse and the probabilistic reasoning method ( prm ), a new self - adaptive kohonen clustering network which overcomes the shortcomings of the conventional clustering algorithms, and the fuzzy neural classifier. the experimental study efface recognition is presented based on the combination of multi - feature multi - classifier. ( 6 ) this paper proposes a hybrid feature extraction method for face recognition, which is a combination of the eigen matrix, fisher discriminant analysis, and the generalized optimal set of discriminant vectors

    （ 5 ）對圖象分類器設計方法進行研究，主要包括：提出了一種基於廣義逆和概率推理的分類器設計方法；提出了一種新的自適應模糊聚類演算法；提出了基於模糊神經網路的分類器設計方法；並對多特徵多分類器組合方法在人臉識別中進行實驗研究； （ 6 ）提出了一種只要一個訓練樣本就能解決人臉識別問題的新方法，該方法結合了特徵矩陣、 fisher最優鑒別分析和廣義最優鑒別分析方法的優點。
  9. Secondly, a multilayered neural network trained with a learning vector quantization ( lvq ) algorithm is applied to pattern recognition of manifestations of the pulse and the classification ability of lvq network is compared with traditional near neighbor algorithm

    其次，本文根據脈圖的時域特徵，採用學習矢量量化演算法，訓練文中確立的神經網路分類器，用以實現對脈圖的識別。並比較了lvq神經網路分類器與傳統近鄰法的分類性能。
  10. The bp neural network is applied to the recognition of wear particles, and a bp neural network sorting system expected to recognize severe wear particle, cutting wear particle, normal wear particle and fatigue wear particle is designed and trained. 6. the function of the neural network ' s hidden layer is analyzed

    將神經網路應用於磨粒識別，設計磨粒分類器，在網路學習中運用改進的bp模型，識別嚴重滑動磨損磨粒、切削磨粒、正常磨損磨粒和疲勞點蝕磨粒，隨機選取50個樣本對分類器進行訓練。
  11. In further research, the following issues must be considered : 1 ) the standardize of corpus ; 2 ) improve the accuracy of chinese words divided syncopation system, handle the different meanings of one word and recognize the words that do not appear in the dictionary ; 3 ) process semantic analysis ; 4 ) dynamically update the training sets fed back by the user ; 5 ) quantitatively analyze the system performance influenced by different factors, use an appropriate model to compare and evaluate the web text classification system ; 6 ) natural language process ; 7 ) distinguish the disguise of sensitive words

    在以後的工作中考慮如下問題: 1 ）數據集的標準化; 2 ）分詞系統精度的提高，對歧義處理以及未登錄詞識別的能力的提高: 3 ）進行合理的語義分析: 4 ）利用用戶反饋信息動態更新訓練集; 5 ）定t分析分類器不同要素對分類系統性能的影響，使用合適的模型來比較和評價分類系統; 6 ）自然語言理解問題，如「引用」問題; 7 ）對于敏感詞匯偽裝的識別問題。
  12. The algorithms of text classification are supervised, which means the classifier training need some human labeled data of fixed classes. generally, the accuracy of classifier is higher with more labeled data. but the labeled data by hand are expensive resource

    文本分類演算法是有監督的學習演算法，它需要一個分類好的，類別已標識的文本數據集訓練分類器，然後用訓練好的分類器對未標識類別的文本分類。
  13. 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。
  14. Usually it consists of two steps : first, obtaining the reference probability from trained classifiers ; second, clustering all mentions that refer to the same entity according to the reference probability

    指代消解過程通常被分為兩個階段：訓練分類器判定指代概率；根據指代概率對實體表達進行聚類。
  15. 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。
  16. The trained classifier can be used to class unknown object samples

    訓練好的分類器便可以用來對未知目標樣本進行分類。
  17. In the process of training classifiers, according to the characteristic of linear discrimination in the samples, different kernel functions and parameters are adopted in each two - class svm

    在訓練svm分類器的過程中，根據舌象樣本中部分類別線性可分，而另一部分類別線性不可分的特點，採用了不同的核函數及其參數。
  18. In the practice stage, an integrated classifier is trained for the current user, whose training set is composed of genuine signatures, and random signatures selected from genuine signatures of existed users registered in the system

    在系統使用階段，對每個用戶，其訓練集中包含該用戶的真實簽名，並抽取系統中已有用戶的真實簽名作為隨機偽造，訓練組合分類器。
  19. From another point, there are a great number of unlabeled documents available online. this paper approach to a novel algorithm, called iterative tfidf, which combines a large number of unlabeled data with small labeled data to train the tfidf classifier

    網上存在大量文本，這些文本一般都沒有類別標簽，該演算法可以利用大量廉價的未標識文本，結合很少的手工標識文本，通過迭代訓練出較高精度的tfidf文本分類器。
  20. Experimental results show that the recognition rate of the proposed classification strategy is higher than that of the single feature domain method, and the strategy is more efficient than the conventional structural neural network

    實驗結果表明，動作分類準確率高於傳統的單特徵集單分類器的分類方法，且訓練、分類效率高於結構化神經網路特徵融合方法。
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