There are two main technologies in time expression recognition:sequence labeling method based on machine learning theory and rule-based method.
时间表达式识别技术主要分为两类：基于机器学习的序列标注方法和基于规则的方法。
Support vector machine (SVM) is the best general machine learning theory developed from statistical learning theory, and suit to do prediction from small samples by learning.
本文采用统计学习理论，建立了基于最小二乘支持向量机的永磁操动机构动作时间预测模型。
The support vector machine(SVM) is a new learning technique based on the statistical learning theory.
支持向量机（SVM）是根据统计理论提出的一种新的学习算法。
Support vector machine (SVM) is a brand-new machine learning technique based on statistical learning theory. It is an ideal facility for modeling of various nonlinear systems.
支持向量机是一种基于统计学习理论的新型机器学习方法，它可以被广泛地用于非线性系统建模。
The support vector machine is a novel type of learning technique, based on statistical learning theory, which USES Mercer kernels for efficiently performing computations in high dimensional Spaces.
支撑矢量机是根据统计学习理论提出的一种新的学习方法，即使用核函数在高维空间里进行有效的计算。
I actually think one way machine learning (or really, more data) will affect the field is that after a while it will re-energize economic theory.
实际上，我认为机器学习(或者说，更多的数据)改变这个学科的方式之一是为经济理论研究重新注入能量。
So I'd say machine learning is guided and inspired by the theoretical results you get from computational learning theory.
所以我想说，机器学习是引导和启发你从计算学习理论的理论结果。
Support vector machine (SVM) is a new generation machine learning technique based on the statistical learning theory.
支持向量机(SVM)是基于统计学习理论的新一代机器学习技术。
However, according to the computational learning theory article, computational learning theory is just a field that is related to machine learning.
然而，根据计算学习理论第，计算学习理论是一个领域，是机器学习相关。
Compared with statistical theory, statistical learning theory focuses on the machine learning of small sample size and can trade off between the complexity of models and generalization performance.
与传统统计学相比，统计学习理论是一种专门研究小样本情况下机器学习规律的理论。
In this paper, statistical learning theory and support vector machine method are introduced in eor potentiality prediction for the first time.
本文首次将统计学习理论及支持向量机方法引入提高采收率方法的潜力预测中。
By using rough set theory, this paper structures classification rules and processes the support vector machine feedback results with learning the train set.
利用粗糙集理论，通过对训练集的学习，构造分类规则，对支持向量机反馈后的结果再次进行处理。
Support vector machine is a new learning method based on VC theory, good generalization is required by minimizing the upper abound of expected risk.
支持向量机是一种基于VC理论的创造性学习方法，它能够使期望风险最小化，具有较强的推广能力。
SVM transforms machine learning to solve an optimization problem, and to solve a convex quadratic programming problem by the optimization theory and method constructing algorithms.
它将机器学习问题转化为求解最优化问题，并应用最优化理论构造算法来解决凸二次规划问题。
As an effect tool of pattern recognition and data processing, rough set theory (RST) and support vector machine (SVM) have become the focus of research in machine learning.
粗糙集理论(rst)与支持向量机(SVM)作为模式识别，数据处理的有效工具，已成为机器学习的研究热点。
Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Support Vector machine is a kind of new machine studying method, which is based on Statistical Learning Theory.
支持向量机是基于统计学习理论的一种新的机器学习方法。
Transductive inference based on support vector machine is a relatively new research region in statistical learning theory.
基于支持向量机的直推式学习是统计学习理论中一个较新的研究领域。
Support vector machines (SVM) are a kind of novel machine learning methods based on statistical learning theory, which has been developed to solve classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Support vector machine (SVM) is a new learning machine based on the statistical learning theory.
支持向量机是一种基于统计学习理论的新型机器学习方法。
Support Vector Machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition.
支持向量机是统计学习理论的一个重要学习方法，也是解决模式识别问题的一个有力工具。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。
This paper introduces statistical learning theory and support vector machine, proposes a new method, support vector machine technology, to simulate quasi-geoid.
介绍统计学习理论和支持向量机，提出利用支持向量机技术进行似大地水准面拟合。
Support Vector machine is one of the hot points in machine learning research, it's theoretical basis is Statistical learning Theory.
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 (SVM) is a novel and powerful learning method which is derived based on statistical learning theory (SLT) and the structural risk minimization principle.
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。
Secondly, the text studies the Statistical Learning Theory(STL) and Support Vector Machine(SVM)theory seriously, discusses multi-category classification algorithms of SVM.
其次，认真研究了统计学习理论的主要内容和SVM算法的基本原理，并且就SVM的多种多类别分类算法分别加以讨论。
SVM is a novel powerful machine learning method developed in the framework of Statistical Learning Theory (SLT).
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。
Support Vector Machine (SVM), based on the counts study theory, is a research hot spot in machine learning domain.
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) is a machine learning method based on Statistical Learning Theory. It can solve a series of issues of Neural Networks.
支持向量机是一种基于统计学习理论的机器学习方法，它解决了神经网络中存在的一系列问题。
Support Vector Machine(SVM) is a machine learning method based on Statistical Learning Theory. It can solve a series of issues of Neural Networks.
支持向量机是一种基于统计学习理论的机器学习方法，它解决了神经网络中存在的一系列问题。
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