英汉例句

• it can solve small-sample learning problems better by using experiential risk minimization in place of structural risk minimination.

  由于采用了结构风险最小化原则代替经验风险最小化原则，使得较好的解决了小样本学习的问题；

• an area based risk minimization principle is proposed.

  提出了一种区域风险最小化的分类器求解策略。

• we discuss the problem of portfolio investment with risk minimization subject to nonnegative investment proportional coefficient.

  研究非负投资比例系数约束条件下，实现风险最小化的组合证券投资问题。

• it can solve small-sample learning problems better by using experiential risk minimization in place of structural risk minimization.

  由于采用了结构风险最小化原则替代经验风险最小化原则，使它较好的解决了小样本学习的问题。

• the main advantage of svm is that it can serve better in the processing of small-sample learning problems by the replacement of experiential risk minimization by structural risk minimization.

  由于使用结构风险最小化原则代替经验风险最小化原则，使它能较好地处理小样本情况下的学习问题。

• the svm (support vector machines) is a classification technique based on the structural risk minimization principle.

  是一种基于结构风险最小化原理的分类技术。

• structural risk minimization induce principle is used to control the bound on the value of achieved risk by controlling experiential risk and belief bound at the same time .

  结构风险最小化归纳原则通过控制经验风险和置信范围来控制实际风险的界。

• compared with multivariate statistics and artificial neural networks, support vector machine based on structure risk minimization has better classification performance.

  与统计分析和神经网络相比，基于结构风险最小的支持向量机有更好的分类性能。

• based on statistical learning theory, support vector machine (svm) can solve the small-sample problems well by using the structural risk minimization principle.

  支持向量机（svm）以统计学习理论为基础，采用结构风险最小化原则，能较好地解决小样本数据学习问题；

• support vector machines is a new general machine learning tool based on structural risk minimization principle that exhibits good generalization.

  基于结构风险最小化原则的支持向量机（ svm）对小样本决策具有较好的学习推广性。

• they uses structural risk minimization and the kernel trick to solve the learning problems.

  它使用结构风险最小化原则，运用核技巧，较好地解决了学习问题。

• we use the theory of local risk minimization for incomplete markets to determine hedging strategies for equity-linked life insurance contracts with stochastic interest rates.

  提出利用不完全市场的局部风险最小对沖方法对沖保险者的风险。

• the difference between them is that the former is based on the structural risk minimization principle and the latter is based on the experiential risk minimization principle.

  不同的是，前者是基于结构风险最小化原理，后者基于经验风险最小化原理。

• support vector machines, the implementation of structural risk minimization (srm) rules, have some attractive merits, such as global optimization, simple structure, generalize abilities and so on.

  支持向量机方法具有全局最优、结构简单、推广能力强等优点，因此作为结构风险最小化準则的具体实现，最近几年得到了广泛的研究与发展。

• because the structural risk minimization principle makes svm exhibit good generalization.

  结构风险最小化原则使其具有良好的学习推广性。

• because neural network is based upon empirical risk minimization and asymptotic theories, it is suitable to deal with situations where the amount of samples is tremendous and even infinite.

  神经网络的理论基础是最小化经验误差，这种基于传统的渐进理论的学习方法，在训练样本点无穷多时是适用的。

• secondly some concepts such as random rough empirical risk functional, random rough expected risk functional and random rough empirical risk minimization principle are proposed.

  提出随机粗糙经验风险泛函，随机粗糙期望风险泛函，随机粗糙经验风险最小化原则等概念。

• based on structural risk minimization principal, the influences of the error penalty parameter c and the kernel parameter σ on support vector machines generalization ability are studied.

  支持向量机是基于结构风险最小化原理的一种学习技术，是一种具有很好泛化能力的预测工具，它有效地解决小样本、非线性、高维数、局部极小等问题。

• using the absolute deviation as a risk measurement index, a novel absolute deviation optimal purchasing portfolio model for multiple markets is built in risk minimization target.

  用绝对离差度量供电公司的购电风险，建立以风险最小化为目标的多市场购电组合优化模型。

• the svm method is based on seeking on the structural risk minimization by few learning samples supporting, and it has important feature such as good generalization and classification performance, etc.

  支持向量机方法基于小学习样本条件下，通过寻求结构风险最小，以期获得良好的分类效果和泛化能力。

• support vector machine (svm) is a new general learning machine, which analyzes the consistency of learning and speed of convergence from structure risk minimization principle.

  支持向量机（svm）是一种新的通用学习机器，它从结构风险最小化的角度，分析了学习过程的一致性、收敛速度等。