In order to increase accuracy in gender classification, an iterative learning approach combining support vector machine (SVM) and active appearance model (AAM) was proposed.
为了提高性别检测的精度,提出了一种支持向量机(SVM)与主动外观模型(aam)相结合的迭代学习算法。
Philips long-distance teaching model in a simulated environment provides technical and intellectual support for autonomous English learning.
菲力普远距离模拟环境教学模式为英语自主学习提供了一个技术支持和智力支持。
This paper's main works is that: learning algorithm studies of support vector machine, mathematical model and application about feature selection, convergence analysis of clustering algorithm.
本文主要致力于支持向量机、近似支持向量机的学习算法研究,特征提取的数学模型与算法的改进及其应用,聚类分析算法的收敛性证明。
应用推荐