In addition,the unique use of asymmetric and elastic kernel function as well as"secondary learning"algorithm and graph theory makes kernel highly adaptable and be able to automatically adjust itself to identifying stage, image content and the image quality.
此外,由于使用了独创的不对称流动核函数和"二次学习"算法并借鉴了图论的思想,此核有很强的适应能力,能自动调整自身以适应:识别阶段(粗识别和细识别阶段)、图像内容(如:识别1和9时核的形态不同)和图像质量(数字扭曲变形程度)。
参考来源 - 基于格式塔心理学的手写识别及MatLab仿真·2,447,543篇论文数据，部分数据来源于NoteExpress
得到了第二次学习的机会，我更加努力地学习，并且逐渐对证据法产生了浓厚的兴趣。
Given a second chance, I worked much harder, becoming fascinated by the law of evidence.
最小二乘支持向量机相比传统的支持向量机，丧失了解的稀疏性，影响了二次学习的效率。
Compared with the classical Support Vector Machines, the Least Squares Support Vector Machines lose the sparseness, which would influence the efficiency of re-learning.
因此，我的理论是边学边用，您可以回顾，第二次可以学习更多。
So, my take-away is that as you learn something and then apply it, you can go back and learn more on a second pass.
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