提出了一种在人脸识别中解决小样本问题的新算法。
A novel algorithm for solving the small sample size problem in face recognition is proposed.
在软测量建模过程中，基于支持向量机的算法能较好地解决小样本、非线性、高维数、局部极小点等问题。
In model establishment of soft-sensing, the problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine algorithm.
本文在中、小样本试验数据下，研究响应模型的选择问题。
Binary response model choice is researched with the data of media or small sample size.
该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
多个人脸数据库上的实验结果表明，本算法能够有效地解决线性判别分析中的小样本规模问题。
Experiments demonstrate that the proposed method can effectively solve the small sample size problem of LDA.
“高维度小样本”问题是模式识别应用中的主要障碍之一。
"High dimensionality and small size samples" is widely encountered in many real world machine learning applications.
“高维度小样本”问题是模式识别应用中的主要障碍之一。
"High dimensionality and small size samples" is widely encountered in many real world machine learning applications.
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