因此，在传统的数学和物理基础课题之外，应该增加一门融合了计算机科学、编程、统计学和机器学习的新学科。
Therefore, a new discipline blending computer science, programming, statistics and machine learning should be added to the traditional foundational topics of mathematics and physics.
将推论统计学应用到Web数据流需要的不仅仅是学习作为各种统计检验基础的数学知识。
The application of inferential statistics to Web data streams involves more than learning the math underlying various statistical tests.
首先概述了本文研究内容的基础—统计学习理论与支持向量机方法，为本文后续的研究方向和内容进行了铺垫。
This paper's basic concepts of Statistical Learning Theory and SVM are summarized firstly, which are the groundwork of next research works.
文章从统计学习理论入手，在讲述SVM一般原理的基础上，分析比较不同种的支持向量机的性能。
Studying from the statistical theory, based on the general principle of SVMs, this paper analyzes and compares the capability of the different kinds of SVMs.
方法以统计学的正态分布与中心极限定理为理论基础，结合免疫进化的思想，提出免疫控制图原理及学习算法；
MethodsBased on the theory of normal distribution and central limit theorem of the statistics, the immune control chart is proposed, which combines the immune idea.
接着对统计学习理论进行了介绍，深入探讨了建立在该理论基础上的SVM算法。
Secondly, the basic knowledge of the statistical learning theory has been introduced and the SVM based on the theory has been gone deep into discussed.
支持向量机是机器学习领域的研究热点之一，其理论基础是统计学习理论。
Support Vector machine is one of the hot points in machine learning research, it's theoretical basis is Statistical learning Theory.
本文在经典统计学习理论的基础上，讨论了可能性空间上学习过程一致收敛速度的界。
In this paper, the bounds on the rate of uniform convergence of the learning processes on possibility space are discussed based on the classic Statistical learning Theory.
建立在统计学习理论基础之上的支持向量机(SVM)，是一种基于结构风险最小的小样本机器学习方法。
Support vector machine (SVM) is a novel and powerful learning method which is derived based on statistical learning theory (SLT) and the structural risk minimization principle.
在对统计学习理论以及相关的优化理论进行回顾的基础上，从四个方面详细描述了SVR模型的基础知识，并指出了SVM的优缺点。
With an overview on the statistical learning theory and the related optimization theory, we expound the basic knowledge of SVR model and point out the advantages and disadvantages of SVM.
学习本课程将使经济学家和其他社会科学研究人员获得扎实的概率与统计学基础知识。
This course will provide a solid foundation in probability and statistics for economists and other social scientists.
文章系统地介绍了支持向量机和其理论基础——统计学习理论。
This paper studies SVM and its theory basic-statistical learning theory.
支持向量机是以统计学习理论为基础的，采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法。
It is a new statistical study method in which the traditional empirical risk minimization principle is replaced by structural risk minimization principle.
该方法以统计学习理论为基础，通过和BP神经网络进行比较的实验，证明其在交通量预测中的有效性。
SVM algorithm is based on statistical theory. Analysis of the experimental results proved that the algorithm of could achieve much effective than that of BP neutral network.
统计学习理论具有坚实的理论基础，为解决小样本学习问题提供了统一的框架。
Statistical Learning Theory is based on a solid theoretical foundation. It provides an unified framework for solving the small sample learning problem.
支持向量机是机器学习领域的一个研究热点，它的理论基础是统计学习理论。
Support Vector Machine (SVM), based on the counts study theory, is a research hot spot in machine learning domain.
本项目以统计学习理论为基础，深入研究了应用支持向量机方法解决机械智能诊断和状态预测的相关问题。
Based on statistical learning theory (SLT), the relevant problems of solving the machinery intelligent diagnosis and condition prediction are thoroughly researched in this project by means of SVM.
支持向量机是在统计学习理论基础上开发出来的一种新的、非常有效的机器学习方法。
SVM is a novel powerful machine learning method developed in the framework of Statistical Learning Theory (SLT).
支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。
Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.
支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。
Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.
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