training sample set 中文意思是什麼
training sample set
解釋
訓練樣本集-
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。 -
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。 -
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。 -
When prediction with little training sample set and large variable si ? is concerned, this paper abstracts the prime factors from the training sample set, then only inputs the prime factors into ann instead of primary variables
對于多指標小樣本預測問題，文中利用主成分分析法對原有指標體系進行處理，提取主成分構成新的指標作為神經網路的輸入。 -
Using error back propagation algorithm. the limitation of the bp net is also improved ; 2 > when prediction with little training sample set and large variable size is concemed, this paper abstracts the prime factors from the training sample set using rough set theory, then only inputs the prime factors into ann. this diminishes the size and the input nodes number of ann
對于即網路本身易陷入局部極小點及收斂速度慢的問題，在文中也得到了改進； 2 、對于財政轉移支付中標準收入的測算屬于多指標小樣本的預測問題，文中首次利用粗集理論對初始的指標體系進行約簡，提取出關鍵性的因素作為神經網路的輸入。 -
After training, the bp can capture the inherent nonlinear mapping relationship held in sample set. while in operation, it can finish any nonlinear mapping from n - dim space of input to m - dim space of output
在運行階段，當向網路輸入訓練時未見過的非樣本時，它便能完成由輸入的n維空間到輸出的m維空間的任意非線性的正確映射。 -
In this dissertation, we propose improved genetic algorithm and utilize it to search sample space for classification and evaluation with the best representative subset of training set
本文提出一種改進的遺傳演算法，利用改進的遺傳演算法搜索樣本空間，將得到的訓練集的近似最優代表性子集作為訓練集去分類評估集。 -
A main method to estimate the effect of neural network training depends on the forecast precision of the test samples, but there is no a proper method to select test samples from the sample set
研究內容主要是神經網路訓練過程中樣本分配問題。網路訓練效果的評價依賴于樣本分配方式，而目前進行樣本分配主要是依靠經驗和技巧的方法存在很大隨機性。
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