局部核方法及其应用

核方法是机器学习中一种强有力的学习算法.多个领域的实践表明，通过将领域知识嵌入到核函数中，一般会得到比较好的学习效果. 从微分流形的观点讨论了机器学习中全局信息与局部信息的关系，并提出了一种嵌入局部有意义信息的核方法.文本分类的实验结果表明，与其它几个分类算法相比，它具有较高的性能.

Locality preserving kernel method and its application

Kernel method is now a powerful altenative in many machine learning tasks. Practice shows that it will achieve better performance if domain knowledge could be incorporated. The relationship between global information and local information when processing data is discussed. A text categorization test is conducted to compare our method with other several classification methods, and one will see that ours outperfonns them.