1) supervised learning
有监督学习
1.
A Method of Choosing Web Services based on Supervised Learning Principle;
一种基于有监督学习原理的Web服务选择方法
2.
By using optimal cluster algorithm in combination with supervised learning of training system, functional approximation efficiency is improved.
本文依据可加性模糊系统理论 ,提出了一种新的预测方法 ,利用聚类方法与有监督学习相结合的训练方法 ,提高了系统的函数逼近能力。
2) learning method with supervision
有监督式学习
1.
It utilized the characteristic that neural network can approach nonlinear function with arbitrary precision,adopted neural network s learning method with supervision,and regarded the prediction error as feedback to adjust the weighted values in flood level prediction network in order to achieve the objective of study.
利用神经网络能以任意精度逼近非线性函数的特点,采用神经网络的有监督式学习,并将预测误差作为反馈来调整水位预测网络中的权值分布,以达到学习的目的。
3) Supervised learning classification
有监督学习分类
4) 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。
5) monitoring learning
监督性学习
6) unsupervised learning
非监督学习
1.
Accelerating the Training of Feedforward Neural Networks Using Improving Unsupervised Learning Principle;
用改进的非监督学习方法加速前馈神经网络的训练
2.
Image object′s semantic hierarchy and its unsupervised learning algrithm
图像目标语义层级结构及其非监督学习算法
3.
Experiments show that these unsupervised learning methods had different characters in classifying land use/cover of remote sensing.
结果表明三种非监督学习方法在进行遥感土地利用/覆盖分类过程中,在分类性能上有显著差异。
补充资料：有监督学习
分子式：
CAS号：
性质：用已知某种或某些特性的样本作为训练集，以建立一个数学模型(如模式识别中的判别模型，人工神经网络法中的权重模型等)，再用已建立的模型来预测未知样本，此种方法称为有监督学习。
CAS号：
性质：用已知某种或某些特性的样本作为训练集，以建立一个数学模型(如模式识别中的判别模型，人工神经网络法中的权重模型等)，再用已建立的模型来预测未知样本，此种方法称为有监督学习。
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