片语

ensemble machine learning集成机器学习

ensemble learning theory集成学习理论

ensemble of learning methods组合学习方法

ensemble learning methods集成学习方法

selective ensemble learning选择性集成学习

Ensemble Learning Approach整体学习方法

Ensemble Learning Algorithm集成学习算法

bagging ensemble learningBagging集成学习

ensemble learning algorithms整体学习演算法

英汉例句

• ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.

  集成学习是当前机器学习的一个研究热点，它可以提高分类算法的泛化性能。

• the key of a good ensemble learning algorithm is able to generate the diversity of individual learners.

  一个好的集成学习算法，关键是能生成差异度大的个体分类器。

• firstly, a review of the most famous previous approaches is made in chapter 2, including maximum a posterior method, ensemble learning method, regularization methods and edge enhancing methods.

  首先，本文将回顾以往的多种相关算法研究，包括最大后验概率算法、变分贝叶斯以及规范化、边缘增强等方法。

• ensemble learning is a general method for classifying data streams.

  对数据流分类分析的常用方法是集成学习。

• it is generally recognized that the key in ensemble learning is to effectively generate individual learners with strong generalization ability and great diversity.

  一般认为，有效地产生泛化能力强、差异大的个体学习器，是集成学习算法的关键。

• generally speaking, this paper contains several aspects:(1) it shows an overview of selective ensemble learning in international research status, background and significance.

  概括的说，本文主要包含几个方面的内容：（1）概述选择性集成学习国内外研究的现状、背景和意义。

• the application of ensemble learning in face recognition is gradually extending and the inherently distinctive of the system improves distinctly.

  因此，很多人开始将集成学习和人脸识别联系起来。

• however, with the increase in individual learner, the diversity between learners is smaller, and ensemble learning accuracy is worse.

  然而，随着个体分类器的增多，个体分类器之间的差异度就越小，集成学习的精确度反而下降。

• the ensemble learning is a hotspot in the intelligent learning field.

  在智能学习领域，集成学习是一个新的研究热点。