1) unsupervised learning
无监督学习
1.
Intelligent fire detection based on unsupervised learning clustering algorithm of Dignet
基于Dignet无监督学习聚类算法的智能火灾探测
2.
The learning of connectionism,which consists mainly of supervised learning,intensive learning and unsupervised learning,is modelled after the learning of human beings.
其学习是对人类学习的模拟,主要有监督学习、强化学习和无监督学习三种。
3.
The result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.
无监督学习环境下的特征选择往往无法取得像有监督学习环境下那样令人满意的效果。
2) supervised learning
监督学习
1.
Land evaluation based on agglomerative hierarchical cluster algorithm combining with supervised learning algorithm;
融合监督学习与凝聚层次聚类的土地评价方法
2.
Aimed at the problem of electroencephalography(EEG) pattern recognition in brain computer interfaces(BCIs),a classification method based on probabilistic neural network(PNN) with supervised learning was presented.
针对脑机接口(BCI)研究中脑电信号(EEG)的模式识别问题,提出了一种基于有监督学习的概率神经网络(PNN)的分类方法。
3.
The learning of connectionism,which consists mainly of supervised learning,intensive learning and unsupervised learning,is modelled after the learning of human beings.
其学习是对人类学习的模拟,主要有监督学习、强化学习和无监督学习三种。
3) Unsupervised learning ANN
无监督学习神经网络
4) unsupervised LVQ
无监督学习矢量量化
1.
This paper presents a generalized formulation of unsupervised LVQ,and transfers classical unsupervised LVQ algorithm into learning vector quantization which is based on typically scaling function,its formulation is very convenient for its extension and its application.
无监督学习矢量量化(LVQ)是一类基于最小化风险函数的聚类方法,文中通过对无监督LVQ风险函数的研究,提出了无监督LVQ算法的广义形式,在此基础上将当前典型的LVQ算法表示为基于不同尺度函数的LVQ算法,极大地方便了学习矢量量化神经网络的推广与应用。
5) Batch-SOM
批处理无监督学习
6) monitoring learning
监督性学习
补充资料：无监督学习
分子式：
CAS号：
性质：在模式识别或自动分类中，所给的学习样本不带有类别的信息，而是根据随机变量本身统计特征来进行分类的算法，称为无监督学习或无监督分析(unsupervised analysis)。聚类分析就是一种无监督分析。
CAS号：
性质：在模式识别或自动分类中，所给的学习样本不带有类别的信息，而是根据随机变量本身统计特征来进行分类的算法，称为无监督学习或无监督分析(unsupervised analysis)。聚类分析就是一种无监督分析。
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