Competitive learning is an unsupervised learning.
竞争学习是一种无监督学习。
Use some unsupervised learning algorithm to label data.
使用无监督学习算法对标签数据。
There are actually two approaches to unsupervised learning.
非监督学习一般有两种思路。
This would be an example of unsupervised learning in a classification context.
这将在后面成为无监督学习上下文分类的一个例子。
An unsupervised learning approach for analysis of human motion is proposed.
提出了一种基于非监督学习的人体运动分析方法。
We need to use more of a combination of supervised and unsupervised learning.
我们需要将监督式和非监督式学习更紧密地结合起来。
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。
In unsupervised learning, the goal is harder because there are no pre-determined categorizations.
对无监督学习来说这个目标很难实现，因为缺乏事先确定的分类。
As an unsupervised learning technique, dustering is a division of data into groups of similar objects.
聚类问题作为一种无监督的学习，能根据数据间的相似程度自动地进行分类。
In unsupervised learning, only learning to network with some samples, rather than provide an ideal output.
在无监督学习中，只向网络提供一些学习样本，而不提供理想的输出。
Unsupervised learning is a good method to solve the problem of recognition of unknown radar emitter signal.
无监督学习是解决未知雷达辐射源信号识别的有效方法。
Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.
无监管学习的常见方法包括k - Means、分层集群和自组织地图。
Exploratory data analysis (unsupervised learning) may be used to guide the choice of suitable learning strategies.
实验数据分析(无监督学习)可以被用来指导选择合适的学习策略。
Unsupervised learning is one of those other types, where you just look at data and discover stuff about the world.
根据数据去探索世界的无监督学习便是其他学习类型之一。
In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data.
可以简单的理解为：非监督学习是指，尝试从未标注的数据中，寻找隐藏的结构。
However, the result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.
但是，无指导学习环境下的属性选择往往无法取得像有指导学习环境下那样令人满意的结果。
In the experiment aspects, the results shows that this algorithm can deal with the unsupervised learning problem successfully.
实验结果表明，该算法能成功地解决很多非监督分类问题。
Unsupervised learning, as you might guess, is tasked with making sense of data without any examples of what is correct or incorrect.
无监管学习的任务是发挥数据的意义，而不管数据的正确与否。
Unsupervised learning is used to adjust input weight values and supervised learning is utilized to adjust output weight values.
学习过程中，采用无监督学习算法对输入权重进行调整，采用有监督学习算法对输出权重进行调整。
I'll focus on the two most commonly used ones - supervised and unsupervised learning - because they are the main ones supported by Mahout.
我将重点讨论其中最常用的两个—监管和无监管学习—因为它们是Mahout支持的主要功能。
Clustering analysis is important part of data mining. It is an unsupervised learning process and it doesn't need prior knowledge about data set.
聚类分析是数据挖掘重要的组成部分，它是一种无监督的学习方法，不需要关于数据集的先验知识。
Now I have done some basic reading on supervised and unsupervised learning algorithms such as decision trees, clustering, neural networks... etc.
现在我已经做了对的监督和无监督学习算法，如决策树，一些基本的阅读聚类，神经网络等。
Based on the framework of network intrusion detection systems based on data mining, this paper devises an analyzer model of unsupervised learning.
本文在基于数据挖掘的网络入侵检测系统框架基础上设计了一个无导师学习的分析器模型。
Clustering analysis is an important research field of data mining, and has been widely used in industries as a kind of unsupervised learning methods.
聚类分析方法是数据挖掘的一个重要研究方向，其作为一种无监督学习方法被广泛应用于各行各业。
A lot of human learning comes from unsupervised learning where you're just sort of observing the world around you and understanding how things behave.
人类的学习有大量内容来自无监督式的学习，也就是说，你只是在观察周围的世界，理解事物的道理。
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 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。
The Word Sense Disambiguation (WSD) study based on large scale real world corpus is performed using an unsupervised learning algorithm based on DGA improved Bayesian Model.
采用基于依存分析改进贝叶斯网络的无指导的机器学习方法对汉语大规模真实文本进行词义消歧实验。
The test proves that if prototype space mapping is reasonable, then this unsupervised learning ANN has good self - learning function. It has good application in engineering.
试验表明，只要样本空间映射合理，这种自组织无监督的神经网络具有很好的自学习功能，在工程中具有广泛的应用前景。
The test proves that if prototype space mapping is reasonable, then this unsupervised learning ANN has good self - learning function. It has good application in engineering.
试验表明，只要样本空间映射合理，这种自组织无监督的神经网络具有很好的自学习功能，在工程中具有广泛的应用前景。
应用推荐