使用无监督学习算法对标签数据。
竞争学习是一种无监督学习。
这将在后面成为无监督学习上下文分类的一个例子。
This would be an example of unsupervised learning in a classification context.
无监督学习是解决未知雷达辐射源信号识别的有效方法。
Unsupervised learning is a good method to solve the problem of recognition of unknown radar emitter signal.
无监督学习：输入数据不带标签或者没有一个已知的结果。
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.
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。
Unsupervised learning is one of those other types, where you just look at data and discover stuff about the world.
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。
In unsupervised learning, the goal is harder because there are no pre-determined categorizations.
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。
Exploratory data analysis (unsupervised learning) may be used to guide the choice of suitable learning strategies.
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。
In unsupervised learning, only learning to network with some samples, rather than provide an ideal output.
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 learning of connectionism, which consists mainly of supervised learning, intensive learning and unsupervised learning, is modelled after the learning of human beings.
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。
Now I have done some basic reading on supervised and unsupervised learning algorithms such as decision trees, clustering, neural networks... etc.
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。
Unsupervised learning is used to adjust input weight values and supervised learning is utilized to adjust output weight values.
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。
Clustering analysis is an important research field of data mining, and has been widely used in industries as a kind of unsupervised learning methods.
自组织神经网络可以进行无监督学习，且可以边学习边工作，因此本文提出的方法追踪迅速，自动化程度高。
The method is rapid in tracking and high in automation degree because the self-organizing neural network can learn without supervision and can learn and work at the same time.
半监督学习算法同时考虑有标记和无标记数据，能显著提升学习效果。
Semi-supervised learning algorithms, which consider both labeled and unlabeled data, can improve learning effectiveness significantly.
半监督学习算法同时考虑有标记和无标记数据，能显著提升学习效果。
Semi-supervised learning algorithms, which consider both labeled and unlabeled data, can improve learning effectiveness significantly.
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