Unsupervised Learning

Constrained clustering and transductive learning mainly deal with learning problems between unsupervised learning and supervised learning.

约束聚类和约束分类主要处理学习问题的方式间于无监督学习和监督学习。

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。

The traditional clustering is based on unsupervised learning mechanism, partitioning objects on the basis of a specific distance or similarity.

传统的聚类算法基于无监督学习机制，仅依据某种特定的距离或相似度进行划分。

Moreover, the studying of unsupervised learning-based monitoring model lays a foundation for the applications of other unsupervised learning models in process quality control.

基于无监督学习的监控模型的研究也为其它无监督学习器在过程质量监控的应用研究奠定了基础。

The process of clustering is dividing the not training samples into different meaningful class clusters, which belongs to unsupervised learning.

聚类过程是将没有训练样本的数据集划分为有意义的不同类，属于无监督学习。

We attempt to learn the robust manifold learning algorithm for different applications by three approaches of de-noising, supervised learning, and unsupervised learning.

分别从除噪、监督学习、无监督学习三个方面入手，研究能适应不同应用场景的健壮流形学习算法。

The paper presents an unsupervised learning algorithm to learn answer pattern for answer extraction module of Chinese Question Answering ( QA).

本文提出了一种基于无监督学习算法的问答模式抽取技术从互联网上抽取应用于汉语问答系统的答案模式。

As an important unsupervised learning algorithm without pre-marked samples in machine learning, clustering has been well developed in recent years.

聚类作为机器学习中一种重要的无监督学习算法，具有无需事前标记样本类型等优点，在近些年得到了很好地发展和应用。

In the algorithm, improved genetic algorithm ( GA) was used to optimize the parameter, and unsupervised learning Kohonen neural network was used to cluster the parameter.

在算法中，运用改进的遗传算法（Genetic Algorithm，GA）进行参数的优化，运用无监督学习的Kohonen神经网络进行参数的聚类。

The supervised and unsupervised learning diagnosis methods are discussed and several improvements have been presented in the learning algorithms.

本文对模拟故障诊断的有监督学习和无监督学习方法分别进行了研究，通过对实现过程的分析，对经典的学习算法进行深入研究，并提出若干改进。

Sources number estimation and blind separation algorithm based on unsupervised learning

基于无监督学习的源数估计及盲分离算法

The methods of BSS and ICA are belong to a class of unsupervised learning algorithm, its algorithm theory and practical application are related the optimization of a number of areas of mathematics and neuroscience.

目前，ICA已经成为盲源分离的主流方法。BSS与ICA都属于一类无监督学习算法，他们的算法理论与实际应用涉及到优化数学和神经科学等多个领域。

An Unsupervised Learning Algorithm Based on Classification Weight and Mass Center Driving

基于分类权与质心驱动的无监督学习算法

The paper puts forward the method that based on the neural network unsupervised learning, also, improves the index on separation effects.

提出基于神经网络无监督学习的盲分离方法，并改进了分离效果评判指标。

Multiple Attribute Information Decision Making Mode and Application Via Improved Fast Unsupervised Learning Algorithm

多属性信息决策的改进无监督学习算法建模与应用

Unsupervised learning based for part of speech disambiguation in English Chinese machine translation

英汉机译中一种基于无监督学习的词类消歧策略

Research of Blind Source Separation Technology which Based on Unsupervised Learning

基于无监督学习的盲信号源分离技术研究

As an unsupervised learning method, cluster analysis is one of the most important research fields in machine learning.

聚类分析作为一种无监督学习方法，是机器学习领域重要研究方向之一。

In this paper, a fast learning algorithm was proposed on the Oja's rule of unsupervised learning principle and delta rule of supervised learing principle The synaptic weights of the networks is initiated with them.

本文提出一种基于非监督学习的Oja规则和有监督学习的delta规则的快速学习算法.用改进后的结构和学习方法初始化前馈神经网络的权值；

Classification accuracy of unsupervised learning algorithm is usually not satisfactory, so people tend to make use of supervised learning in practice.

由于无监督学习的准确率通常不能令人满意，在实际应用中人们趋向于运用有监督方法。

Semi-supervised learning flourishes as it can circumvent the limitations of unsupervised learning and supervised learning.

半监督学习的蓬勃发展规避了无监督学习和监督学习的局限性。

The self-organizing maps ( SOM) is an unsupervised learning algorithm, which is capable of self organization and visualization and has been used in many areas.

自组织映射（Self-Organizing Maps，SOM）算法是一种无导师学习方法，具有良好的自组织、可视化等特性，已经得到了广泛的应用和研究。

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。

From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning.

从机器学习的观点来看，类相当于隐藏模式，寻找类是无监督学习过程。