例句與造句

1. super efficient solutions of preinvex set-valued vector optimization problems
   預(yù)不變凸集值向量?jī)?yōu)化問(wèn)題的超有效解
2. existence of solutions of implict multi-valued vector equilibrium problems
   廣義向量平衡問(wèn)題解的存在性定理的等價(jià)條件
3. an online algorithm for complex independent component analysis was proposed based on the complex nonlinear functions and the decorrelation of two complex-valued vectors
   摘要基于復(fù)向量不相關(guān)特性和復(fù)值非線性函數(shù)，提出一種在線復(fù)值獨(dú)立分量分析算法。
4. in training phase, singular value vector of a lot of face image information matrix from face image sets constitutes face image sets cha racteristic matrix as a column
   在訓(xùn)練階段，從人臉圖像集中選取大量人臉圖像的信息矩陣的奇異值向量作為矩陣中的一列而構(gòu)成的人臉圖像集特征矩陣。
5. the main idea is to make full use of the decorrelation of two complex-valued vectors in generating independent components by non linear decorrelation
   結(jié)合非正則復(fù)向量的協(xié)方差矩陣和偽協(xié)方差矩陣構(gòu)造出了新的代價(jià)函數(shù)，進(jìn)而提出新算法，通過(guò)復(fù)非線性不相關(guān)，從混合信號(hào)中提取出復(fù)值獨(dú)立分量。
6. It's difficult to find value vector in a sentence. 用value vector造句挺難的
7. the method used here is to recursively form cross products of the values stored in each of the gui-control-possible-value vectors we will call these vectors, " permutation groups "
   此處使用的方法是遞回形成儲(chǔ)存在每個(gè)gui控制項(xiàng)可能值vectors中的值的積（我們稱(chēng)這些vectors為“置換群（permutationgroup）”）。
8. at last, we trained the svm ( support vector machine ) with the power value vectors which the training examples project on the face features subspace . after this training process we use the svm obtained to classify face regions and non-face regions . in this
   最后,用人臉訓(xùn)練樣本在這些人臉局部特征子空間上的投影得到的權(quán)值向量訓(xùn)練支撐向量機(jī)svm(supportvectormachine)獲得最終的人臉檢測(cè)分類(lèi)器。