Supervised learning is tasked with learning a function from labeled training data in order to predict the value of any valid input.
监管学习的任务是学习带标签的训练数据的功能，以便预测任何有效输入的值。
The robot used this data as an input for its machine learning algorithms and created a map between its facial expressions and the movements of its muscle motors.
该机器人用这些数据作为其机器学习算法的输入数据，产生一个脸部表情和相应肌肉马达运动之间的信息存储分布图。
This article describes a new type of fuzzy system with interpolating capability to extract MISO fuzzy rules from input output sample data through learning.
描述了一个通过学习从输入输出采样数据中提取MISO模糊规则的具有插值性能的新型模糊系统。
Unsupervised Learning: Input data is not labelled and does not have a known result.
无监督学习：输入数据不带标签或者没有一个已知的结果。
Semi-Supervised Learning : Input data is a mixture of labelled and unlabelled examples.
半监督学习：输入数据由带标记的和不带标记的组成。
Semi-Supervised Learning: Input data is a mixture of labelled and unlabelled examples.
半监视学习：输入数据由带符号的和不带符号的组成。
Semi-Supervised Learning: Input data is a mixture of labelled and unlabelled examples.
无监督学习：输入数据不带标签或者没有一个已知的结果。
It get the nonlinear mapping to describe the relation of the system's input and output by learning the controlled system's input and output data.
它依据被控系统的输入输出数据，通过学习得到一个描述系统输入输出关系的非线性映射。
Without the destruction of single neurons based on input weights, adopt data pretreatment methods to reduce the number of input layers, so as to improve the ability of evolutionary learning.
在不破坏单个神经元的输入权值的基础上，采用数据预处理的方法来减少输入层的个数，从而提高进化学习的能力。
The good localization characteristics of wavelet functions in both time and frequency space allowed hierarchical multi-resolution learning of input-output data mapping.
利用小波变换所具有的良好的时频分析特性，实现了输入输出之间映射关系的多分辨学习。
Unsupervised Learning : Input data is not labelled and does not have a known result. A model is prepared by deducing structures present in the input data.
如同聚类方法，降维方法试图利用数据中的内在结构来总结或描述数据，所不同的是它以无监督的方式利用更少的信息。
The ANN calculation and decision module has self-learning ability with new input data which can increase the accuracy of the calculated results for the FEM elements.
ANN计算和决策模块具有关于新输入数据的自学能力，该自学能力可以提高FEM元素的计算结果的精度。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
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