training classifier

So-called double sample training was training classifier using face and non-face samples. The required parameters were obtained in a cross-training method.

所谓双样本训练，就是分类器训练时采用人脸与非人脸两类样本，经多次交叉确认法训练得到所需参数。

NPA is currently one of the best training algorithm for support vector machine classifier.

支撑向量机是90年代中期发展起来的机器学习技术,NPA算法是目前最优秀的学习算法之一.

Traditional SVM trains Support Vector Machines classifier directly under linear condition to sort face images and non-face images, and for such kind of classifier, the training is difficult. The amount of compute is big and the speed of training is slow.

传统的SVM直接在线性条件下训练SVM分类器完成人脸与非人脸的分类，分类器训练困难，计算量大且速度慢。

Based on different base classifier algorithms, experiments evaluate performance on three semi-supervised learning algorithms including Self-Training, Co-Training and Tri-Training.

对三种半监督学习算法Self-Training、Co-Training和Tri-Training结合不同的基分类器通过实验来进行性能评估。

Finally, we have built a Random forest classifier on the new training sets to realize the R-gene classification.

最后在重建的训练集上，利用随机森林算法构建可以识别抗性基因的分类器。

Then, as for the T-F units with obvious harmonicity and large energy, their features are replicated in the process of classifier training, and therefore, the classifier can better describe this sort of features.

然后，对于谐波特征明显且能量大的时频单元，在分类器训练阶段复制它们的特征，从而使得分类器对此类特征有更好的刻画能力。