訓練樣本集 的英文怎麼說

中文拼音 [xùnliànyàngběnjí]
訓練樣本集 英文
training sample set
  • 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...
  • 樣 : Ⅰ名詞1. （形狀） appearance; shape 2. （樣品） sample; model; pattern Ⅱ量詞（表示事物的種類） kind; type
  • 本 : i 名詞1 （草木的莖或根）stem or root of plants 2 （事物的根源）foundation; origin; basis 3 （本錢...
  • 集 : gatherassemblecollect
  • 訓練 : train; drill; manage; practice; breeding
  • 樣本 : sample book; specimen; advanced copy; sample; muster; scantling; instance; statistics
  1. The experimental results indicate that it is easy to be realized, can save the calculating cost and improve the constringency speed

    試驗結果表明，用粒子群演算法來訓練樣本集具有容易實現、節省計算成本和提高收斂速度等優點。
  2. Mining classification rules is a procedure to construct a classifier through studying the training dataset. it is a very important part of data mining and knowledge discovery

    分類規則挖掘則是通過對訓練樣本數據集的學習構造分類規則的過程，是數據挖掘、知識發現的一個重要方面。
  3. The system firstly learns the domain training samples by using thesaurus to process word - separation and word - frequency statistics. according to word - frequency distribution, it chooses the feature collection and their weights to formulate feature vector and generate domain model and user model

    系統首先對領域訓練樣本進行學習，利用領域詞典對訓練文本進行詞條切分和詞頻統計，並根據詞頻分佈，提取代表採集目標的特徵項集和相應的權重，生成特徵矢量，形成初始領域模型和用戶模型。
  4. The dada acquisition and pressure survey methods during under - balanced drilling are researched, which provide the foundation for obtaining training samples for neural network

    研究了欠平衡鉆井數據採集和壓力測量方法，為神經網路獲取訓練樣本奠定了基礎。
  5. Image targets swatch set is carve up to several subsets firstly, and each subset is corresponding to one output port of network. then a bp neural network can be constructed to complete vehicle target recognition. with the above method, image targets of several types of vehicles are tested in

    其基本思想是，先用改進的c -均值動態聚類方法將車輛目標的訓練樣本集劃分成若干子集，每個樣本子集對應神經網路的一個輸出埠，以此原則來構建一個bp神經網路，再代入樣本進行訓練后即可用於識別未知的車輛目標樣本。
  6. The selection of classify attribute from web page training - set base on rough sets

    基於粗糙集的網頁訓練樣本集的分類屬性的選擇
  7. Through frequent testing on the temperature of the objects, the training sample assemblies are obtained

    通過對場景中物體的表觀溫度進行多次測量，得到訓練樣本集合。
  8. Another understanding about intrusion detection is viewing machine learning as a searching process, that is to say, intrusion detection is in essence the searching or approximation issue of intrusion rules in accordance to established searching strategy. after some concerned

    在基於遺傳學習的入侵檢測研究中，把機器學習看作一個搜索過程，即入侵檢測可視為基於訓練樣本集，按照既定的搜索策略對入侵規則的搜索或逼近問題。
  9. The numbers of the training sample of the networks are 37, 32, 27, 22 and the numbers of the test sample are 5, 10, 15, 20, respectively, and the relative errors of the predications are less than 3 %. it is shown that the rough network is accurate and available for prediction of stock market

    訓練樣本集的天數分別是37天， 32天， 27天和22天，預測樣本集的天數分別是后5天， 10天， 15天和20天，預測的相對誤差都小於3 。
  10. Firstly, the samples are divided into several sample set, every sample set is represented by one modular e - hmm, the modular e - hmm parameters are obtained through doubly embedded viterbi training algorithm, and the necessary temporary parameters in the parameter re - estimating process are also saved for the use of next step

    該方法對訓練樣本中不同訓練樣本集，都通過訓練演算法產生相應的模型參數模塊，訓練結束后對不同的模型參數模塊合成，得到最終的模型。
  11. Thus the scale of the training data set is reduced greatly and the training speed of svm is improved enormously. because the decision boundary of svm is only determined by support vectors, the classification accuracy is almost preserved when other samples are omitted

    在svm學習之前，首先剔除訓練樣本集中距離判決邊界遠的樣本，選取靠近判決邊界的樣本構成有效訓練樣本集，然後用svm對有效樣本集進行學習，這樣大大降低了訓練樣本集的規模，提高了svm的學習速度。
  12. This paper summarized a system feature set for transient stability classification, several methods for analyzing the separability of input space of transient stability classification are discussed, tabu search - ing technique is employed to select an effective set of features from a large initial features set. the classification test shows that the presented method works very well for feature selection. fisher linear recognition is employed to cut down the training sample set, the computation burden of the ann training is alleviated very much, so the convergence performance is improved

    本文總結提出了一組用於穩定分類的系統特徵，研究了幾種暫態穩定分類輸入空間可分性分析的方法，並利用tabu搜索技術從一個維數較大的特徵集中選擇出一組有效特徵，取得了良好的效果；研究提出了利用fisher線性識別技術壓縮訓練樣本集的方法，大大減輕了ann的訓練負擔，提高了ann收斂的性能。
  13. Some new ideas are proposed in this thesis based on svm and ica : firstly, a modified svm method based on posteriori probability theory is given, which makes the classification super plane corrected from the original one. a better classification result is obtained without finding the best quadric optimization algorithm and large scale training datasets are reduced to small scale training datasets at the same time. secondly, ica is applied to the preprocessing period of the recognition character images for purpose of feature extraction and dimension reduction

    本文在系統研究svm和ica的基礎上提出了以下新的觀點：其一是採用了引入后驗概率的修正svm方法，它在原分類超平面的基礎上不斷修正分類超平面，提高分類正確率，從而避免了尋找最優二次規劃的麻煩，同時將大規模訓練樣本集化為小規模訓練樣本集；其二是應用獨立分量分析ica對需要進行識別的字元圖像預處理，提取字元特徵，降低輸入數據的維數，從而可以為下一步的svm識別過程提供好的數據集，用以提高識別率和識別速度。
  14. After researching the application of artificial neural network to fault diagnosis of transformer, the back propagation algorithm is improved observably. inducting chaos dynamics principium to create chaos neural network back propagation algorithm and using the pretreatment of training sample, the emulation program show that this method is effective

    本文深入研究了人工神經網路在變壓器故障診斷中的應用，對目前最常採用的bp演算法做出了較大改進，在原有演算法中引入混沌動力學原理構造了混沌神經網路bp演算法，並提出了訓練樣本集的預處理方法，在模擬訓練中取得了滿意的效果。
  15. Meanwhile, the svm ' s parameters selection method and the representations of different models are researched. ( 5 ) three types of flood forecast models based on svm are presented. these models are svm flood forecast model with changeless training set, dynamic recursion svm flood forecast model with fixed length training set and dynamic recursion svm flood forecast model with memory

    （ 5 ）根據支持向量機的特點，建立了固定訓練樣本集的svm洪水預報模型、固定訓練樣本集長度的動態遞推svm洪水預報模型和帶記憶的動態遞推svm洪水預報模型三種基於svm的洪水預報模型，它們在實例中的表現體現出了良好的應用前景。
  16. Normal behavior and anomaly are distinguished on the basis of observed datum such as network flows and audit records of host. when a training sample set is unlabelled and unbalanced, attack detection is treated as outlier detection or density estimation of samples and one - class svm of hypersphere can be utilized to solve it. when a training sample set is labelled and unbalanced so that the class with small size will reach a much high error rate of classification, a weighted svm algorithm, i

    針對訓練樣本是未標定的不均衡數據集的情況，把攻擊檢測問題視為一個孤立點發現或樣本密度估計問題，採用了超球面上的one - classsvm演算法來處理這類問題;針對有標定的不均衡數據集對于數目較少的那類樣本分類錯誤率較高的情況，引入了加權svm演算法-雙v - svm演算法來進行異常檢測;進一步，基於1998darpa入侵檢測評估數據源，把兩分類svm演算法推廣至多分類svm演算法，並做了多分類svm演算法性能比較實驗。
  17. First, the fault type is identified by rough - set, then the neural networks determines the fault elements by the sampling voltage values. with regarding to the other method, the rough - set is looked as the pre - system of neural network. the fault information as well as the sampling voltage values are simplified by rough - set, and the outcome of this processing is the input of neural network.

    整體結合是利用粗糙集對故障信息中樣本的所有故障徵兆進行數據處理，通過知識約簡，刪除多餘的徵兆屬性，簡化知識表達空間維數，簡化以後的樣本數據作為神經網路的訓練樣本，構成完整的粗糙集-神經網路故障診斷方法，將粗糙集與神經網路相結合，簡化了神經網路結構，從而達到提高診斷速度的目的。
  18. Statistical parser relies on using many hand - parsed sentences as training examples. however, the task of labeling so many sentences is a labor - intensive task

    大多數現有的句法分析是基於統計方法的，基於統計的句法分析模型需要大規模的訓練樣本，而標注一個大規模樣本集需要很大的人力。
  19. The covariance of the training samples which are sampled from the antenna arrays is delivered into svm training machine after transformed suitably, then we use svm regression on the unknown samples according as the trained machine for getting the source location

    把陣列天線採集的訓練樣本的協方差矩陣進行適當變換之後送入svm訓練器進行學習，然後根據這個學習機對未知樣本進行svm的函數擬合，得到信源方向。
  20. 12 impress modulus are induced from a fortran program, thereby we can get the training samples to train the neural networks

    用一個fortran程序推導出了其對應的12個壓痕模量，從而獲得訓練」樣本集，由此訓練神經網路。
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