Machine Learning Techniques 笔记：2-2 Dual Support Vector Machine
找出最佳的线，margin最大，通过QP求解 非线性特征转换，只需要将原来的x转换为z即可。z空间的线性分类，对应到原来的x空间，可能就是一个非线性的分类动作 透过SVM，large margin，来控制模型的复杂度。透过特征转换得到弯曲的边界，做好Ein 如果d quota的维度很大时，比如无穷大，则可能无法做好。怎么办？ 目标：只跟资料数量有关，与特征的维度无关 原来的问题：有条件的最佳化问题
相关文章
- 1. Machine Learning Techniques -2-Dual Support Vector Machine
- 2. Machine Learning Techniques 笔记：2-6 Support Vector Regression
- 3. Machine Learning Techniques 笔记：2-4 Soft-Margin Support Vector Machine
- 4. Machine Learning Techniques 笔记：2-3 Kernel Support Vector Machine
- 5. Machine Learning Techniques 笔记：2-1 Linear Support Vector Machine
- 6. Machine Learning 06 - Support Vector Machine
- 7. Machine Learning in Action(6) —— Support Vector Machine
- 8. Machine Learning Series No.3-- Support Vector Machine
- 9. Structured Support Vector Machine
- 10. Support Vector Machine
- 更多相关文章...
- • Docker Machine - Docker教程
- • ASP.NET Razor - 标记 - ASP.NET 教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • Java Agent入门实战（一）-Instrumentation介绍与使用
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Machine Learning Techniques -2-Dual Support Vector Machine
- 2. Machine Learning Techniques 笔记：2-6 Support Vector Regression
- 3. Machine Learning Techniques 笔记：2-4 Soft-Margin Support Vector Machine
- 4. Machine Learning Techniques 笔记：2-3 Kernel Support Vector Machine
- 5. Machine Learning Techniques 笔记：2-1 Linear Support Vector Machine
- 6. Machine Learning 06 - Support Vector Machine
- 7. Machine Learning in Action(6) —— Support Vector Machine
- 8. Machine Learning Series No.3-- Support Vector Machine
- 9. Structured Support Vector Machine
- 10. Support Vector Machine