1) learning from fuzzy examples
模糊示例学习
1.
Study on a method in pulverized coal combustion diagnosis based on learning from fuzzy examples;
一种基于模糊示例学习的煤粉燃烧状态诊断方法的研究
2.
This thesis gave the forecast models of patients survivor life based on learning from fuzzy examples and exponential regression,and got the joint forecast model through optimizing the two kinds of models.
基于模糊示例学习与指数回归理论分别得到患者生存寿命的预测模型,并对2种模型进行优化组合,提出联合预测模型,其预测结果可为患者选择治疗方案提供必要的信息。
3.
We designed a fuzzy model by ID3algor ithm based on learning from fuzzy examples.
基于模糊示例学习,利用模糊ID3算法,提出了蠓虫分类模型。
2) learning from examples
示例学习
1.
Study on learning from examples based on rough sets theory.;
基于粗糙集理论的示例学习研究
2.
Integer- programming model for learning from examples and feature subset selection based on extension matrix;
示例学习与特征选择的规划模型方法
3.
To discern positive and negative example fully, feature subset selection plays a great role in learning from examples.
特征选择是示例学习的关键 ,直接关系到获取的概念的优劣。
3) multi-instance learning
多示例学习
1.
A method based on multi-instance learning to improve the itembank redundancy checking algorithm is proposed.
基于多示例学习方法对题库重复性检测算法进行了改进,其基本思想是:将包含多个子问题的试题重复性检测转化为多示例学习问题。
2.
Chinese web index page recommendation, is presented and then addressed through transforming it to a multi-instance learning problem.
多示例学习为中文 Web 挖掘提供了一种新的思路。
3.
Basing on the techniques of immune evolution and multi-instance learning and focusing on changing environments, large scope environments and unknown environments, this dissertation revolves the localization and path planning problems, which a.
本文针对大范围环境、变化环境和未知环境,以免疫进化和多示例学习作为支撑技术,围绕移动机器人在它的运动过程中始终需要解决的定位与规划二个关键问题进行了比较深入的研究,其研究内容涉及基于多图像的定位、并发定位与建图、路径规划、进化与免疫计算和多示例学习等。
4) multiple-instance learning
多示例学习
1.
A method for image retrieval based On salient points feature multiple-instance learning;
基于显著点特征多示例学习的图像检索方法
2.
In the multiple-instance learning, many feature are irrelevant to find the target function, and the Citation-KNN algorithm is highly sensitive to the curse of dimensionality, so the FS -Citation -KNN algorithm was proposed based on the feature selection.
在多示例学习中,有许多属性相对于我们发现目标函数来说是无关的,而且就Citation-KNN算法而言,该算法对维度灾难的问题是十分敏感的,由此本文提出了一种基于特征选择的FS-Citation-KNN算法,该算法不仅考虑了特征选择的问题,还考虑到对于待测包其近邻的距离对于分类的影响。
5) fuzzy learning
模糊学习
1.
In this paper, a web page classification with feature selection and fuzzy learning is proposed.
本文提出了一种基于相似度的特征选择算法和适应模糊学习算法来实现分类 。
2.
The design and implementation of a phosphorus removal fuzzy controller with fuzzy learning mechanism are discussed.
文章系统地介绍了具有模糊学习机制的污水脱磷模糊控制器的设计与实现，提出了模糊学习的概念、原理和方法，给出了基于模糊控制表（查询表）的学习算法。
3.
Fuzzy learning vector quantization(FLVQ) algorithm outperforms the hard competitive vector quantization in that it reduces the dependence of the resulting codebook on the initial codebook selection, yet it has the disadvantages of slow convergence and easy to be trapped in local minima.
模糊学习矢量量化算法 (FL VQ)虽然解决了硬的竞争学习对初始码本的依赖性问题 ,但收敛速度变慢 ,且仍无法克服陷入局部最小 。
6) positive-case oriented learning
面向正示例学习
补充资料：部分学习与整体学习
部分学习与整体学习
part learning and whole learning
部分学习与整体学习(part learningand whole learning)在运动学习和记忆学习中，根据对学习内容的处理方式可以分成部分学习和整体学习。部分学习就是将材料分成几个部分，每次学习一个部分:整体学习就是每次学习整个材料。一般来讲，整体学习的效果优于部分学习。但是，课题复杂彼此没有意义联系的材料，用部分学习的效果好:课题简短或具有意义联系的材料，用整体学习的效果好。在进行学习时，可以将部分学习与整体学习结合起来，先进行整体学习再进行部分学习，或者相反。这种相互结合的学习方式叫做综合学习，效果更好些。 (周国帕撰成立夫审)
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条