training data 中文意思是什麼
training data
解釋
培訓用數據-
The three kinds of simulated point targets are designed and the rangeprofiles at aspect angle are computed. the radar target recognition method based on the optimal cluster centers is simulated and studied. it is discovered that the algorithm is effective when there are lots of training data, but noneffective when there are only a few training data
2 、對最優聚類中心目標識別法進行模擬實驗並研究其識別性能，實驗結果表明該演算法在大樣本訓練數據時能得到較高識別率，是一種有效、可行的識別演算法，但在少樣本訓練數據時，所得識別率急劇下降。 -
Compare with former method it ' s merits are simple, pellucid and this method can be appended new classifiers without affecting former training data
與前一種方法相比，它的優點是簡單、易懂，而且這種方法還可以在不影響已有數據的情況下添加新的分類器重新組合。 -
The method based on statistics has the problem of training data ' s rarefaction, and what restricts the more progress of corpus is the too large workload of manual tagging
基於統計的漢語自動分詞方法存在訓練數據稀疏的問題，而人工標注工作量過大又制約著語料庫規模的進一步擴大。 -
Traditional multiuser detector make good use of all signals which resuilt in multiple access interference so that it provides optimum mai resistance. ( 1 ) nevertheless, it assumes that the receiver can acquire the signature waveform and timing of desired user and the interfering users ; ( 2 ) it has no ability to suppress intercell multiple access interference ; ( 3 ) it cannot be applied in downlink channels. adaptive multiuser detector eliminates the need to know the signature waveforms and the timing of the interferes and has to need training data sequences for every active user
傳統多用戶檢測在單用戶檢測技術基礎上，充分利用造成多址干擾的所有用戶的信息進行聯合檢測，從而具有良好的抗多址干擾能力，但存在一些缺陷： （ 1 ）不僅要求知道期望用戶的地址pn碼及其定時信息，還要求其他干擾用戶的地址pn碼及其定時信息； （ 2 ）不能消除其他相鄰小區的多址干擾對本小區的影響； （ 3 ）不能直接應用在cdma移動通信系統中的下行鏈路。 -
It supports the man - machine interaction, from low level of primitive image features to get high level of logical features, and then studies the training data via man - machine interaction, finally mines knowledge and model needed. the simple hierarchical modeling is suitable for different application in remote sensing image and for the development of remote sensing technology
在分層模型支持下進行互動式學習的挖掘，即從遙感圖像中低層次的原始特徵出發，提取得到高層次的邏輯特徵，通過人機交互，學習用戶提供的訓練數據，挖掘用戶所需要的知識和模式，這種方法能夠適應遙感圖像的不同應用需求。 -
Point training data can be converted to a fuzzy set, therefore the new method can predict both point events and area - events
因此，該方法可以對面事件、點事件和線事件進行評價和預測。 -
But the frequent use of training sequence is certainly a waste of channel bandwidth. research shows that with the prior knowledge of only the signature waveform and timing of the user of interest, blind adaptive multiuser detector can effectively detect data symbol of the desired user without training data sequence for every active user
在傳統多用戶檢測技術基礎上，自適應多用戶檢測利用訓練序列在僅知道期望用戶地址pn碼及其定時信息條件下就可以進行檢測，不足的就是訓練序列佔用了額外的頻率資源。 -
3 ) semantic classification model based som network we use the classification model to combines attributes within a database. this is done using an unsupervised learning algorithm. the output is used as training data for the next stage
3 ）基於som網路的語義分類模型設計建立som網路模型，將元數據特徵向量進行分類，形成bp網路的目標向量，用於匹配規則的提取。 -
Under the different work conditions of the hi - vap cooling, the temperature of the bof shell was predited by means of bp network, and making use of the fem to calculate and collect training data
用有限元計算採集訓練數據，以bp網路為手段，對不同水流通量、不同時刻的汽霧冷卻轉爐爐殼的瞬態溫度進行了預報。 -
The case table includes the training data that you will use to train the mining model
事例表包括要用來為挖掘模型定型的定型數據。 -
4 ) semantic discovering and matching model based bp network the classifier output is used as training data for a bp neural net. the net produced by this can recognize attributes within the database based on their metadata and emerge learning rules
4 ）基於bp網路的語義發現和匹配模型設計建立bp網路模型，通過對樣本數據進行學習進而形成匹配規則，用於異構數據庫之間的語義匹配。 -
Fnn efficiently maps the complex non - linear relationship of training data for its automatic learning, generation and fuzzy logic inference
模糊神經網路具有很強的自學習、泛化和模糊邏輯推理功能，可以有效地映射出訓練數據之間復雜的非線性關系。 -
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。 -
Realization of improved bp algorithm - single output three layers " artificial neural network generator base on improved bp algorithm has been developed by the author, and the generator has some functions that the number of neuron in first and second layer and theirs related training parameters such as learning rate. momentum factor a and the value of sum error e can all be self - defined by the users ; connection weights and threshold in each layer ' s neuron training data and teaching signals can also be input or modified in the friendly interface
生成器功能是:網路結構中的第一、二層神經元個數和訓練參數中的學習速率粉，動量因子a和期望誤差值:可由用戶在一定范圍內自定義;各層的權值、閥值、網路初始樣本值及教師值可在友好的界面下輸入、修改。 -
This metric is optimal in the sense of global quadratic minimization, and can be obtained from the clusters in the training data in a supervised fashion
這個度量導入二次最佳化問題的解，將訓練樣本類結構的傾斜最小化。 -
The proposed indicator kriging classification algorithm has the following advantages : ( 1 ) it can deal with anisotropic problem in feature space, ( 2 ) it is a nonparametric method, and need not to know the type of probability distribution, and ( 3 ) it yields 100 % classification accuracy for the training data
此分類法具有如下之優點： （ 1 ）可處理特徵空間中非等向分佈之問題， （ 2 ）該方法屬于無母數分類法，不需假設各類特徵之機率分佈類型， （ 3 ）對訓練像元之分類正確率可達100 。 -
When both evidence and training data are fuzzy sets, the new method acts as a dual fuzzy weights of evidence method
該方法可以處理訓練層和證據層均為模糊集合的情況，被稱為雙重模糊證據權方法。 -
In the applied part, kernel method is used to improve the method of classification with one class training data. the improved method is combined with ground plane transformation method, so that information from monocular vision and stero vision can be fused effectively. based on this, a demo system of obstacle detecting in outdoor scenes is developed
在實踐應用部分，本文利用核函數方法，改造了現有的單類判別方法，並結合雙目視覺技術中的重投影方法，實現了單、雙目信息的有效融合，研製了一個自然場景下的障礙檢測實驗演示系統。 -
Moreover, this method is robust to the variety of snr and avoids overfitting and local minimum in neural nelwork. the percenlage of correcl idenlificalion for signals is salisfied wilh the fewer training data
該方法在信噪比變化范圍較大的情況下，採用較少的訓練數據就可以達到令人滿意的識別正確率。 -
Based on these descriptions, a nd model called support vector data description ( svdd ) is founded. ( 2 ) a qualitative guide for setting those parameters in oc - svms is investigated. a multi - layer high - speed training strategy was proposed to enable support vector algorithm to handle large training data
（ 2 ）通過分析支持向量機中模型參數對檢測結果的影響，給出了確定這些參數的一般方法;提出了一種分層式的快速訓練方法，克服了樣本個數和維數對支持向量演算法應用的制約，提高了訓練效率。
分享友人