文件名称:staticlearningwithekernelmethods
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- 人工智能/神经网络/遗传算法
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- 2012-11-26
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统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了
一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比
如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法：
支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当
今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified fr a mework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.
一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比
如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法：
支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当
今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified fr a mework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.
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统计学习及核方法简介.pdf