unsupervised learning
英 [ˌʌnˈsjuːpəvaɪzd ˈlɜːnɪŋ]
美 [ənˈsupərˌvaɪzd ˈlɜːrnɪŋ]
n. 无监督学习
双语例句
- 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。 - 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。 - 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。 - Second, the leap forward in unsupervised learning fits in with his work on cryptanalysis;
第二，在无监督式学习方面的跃进和图灵的密码方面工作符合； - Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering.
决策理论，统计分类，最大似然和贝叶斯估计，非参数方法，非监督的学习与聚类。 - 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 - 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 - 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。 - Appointing to the problems produced by θ, a new unsupervised learning algorithm is developed.
针对θ的引入所带来的问题，提出一种新的无指导学习算法&条件重叠学习算法。 - 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。 - 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。 - According to the orders of supervised learning ways, unsupervised learning ways and reinforcement learning ways, this paper summarizes techniques, methods and applications used in the various learning algorithms and learning systems under three learning ways, respectively.
本文按三种学习方式：有导师学习、无导师学习和加强学习的次序，分别概述了在这些学习方式下各种学习算法和学习系统中所采用的学习技术、方法及其应用。 - 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。 - 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-OrganizingMaps，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。 - 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。 - Fuzzy clustering analysis, as a kind of unsupervised learning methods, is a research hotspot concerning about text categorization. Therefore the research of text categorization based on fuzzy clustering is hence of great theoretical and practical significance.
作为非监督学习方法的模糊聚类分析已成为文本分类研究的热点，对基于模糊聚类的文本分类研究具有重大的理论和现实意义。 - Compared with traditional supervised learning and unsupervised learning, semi-supervised learning is in a rather new field.
目前在机器学习界，主要还是传统的监督学习和非监督学习两大类别，半监督学习还属于一个比较新颖的领域。 - Compared to supervised learning, unsupervised learning of a late start has greater space for its research.
相对于有监督学习来说，非监督学习的研究起步较晚，其研究空间比前者更大。 - For unsupervised learning, the results of clustering will be further analyzed by software expert.
对于无经验数据的情况，聚类的结果将由软件领域专家们进行进一步的分析。 - 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。 - As an unsupervised learning technique, manifold learning has become a hot research direction in recent decades.
流形学习是最近几十年新兴的一个研究领域，它是一种非监督机器学习的方法。 - 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。 - The clustering results from unsupervised learning are often far from the real data clusters.
无监督的聚类的结果很难与数据的真实类别结构一致。 - At first, we proposes a kind of unsupervised learning neural network model with convex constraint which has special structure and can realize the compression of data and reduction process. The main characteristics of the neural network can represent information after being trained.
在基于多维数据分析的神经网络研究方面，首先构造了一种无监督学习的凸约束神经网络模型，该网络具有特殊结构，能实现数据压缩与还原过程，经过训练后可以表示信息的主要特征。 - However, the classification ability of LPP is weak dues to that it is an unsupervised learning algorithm.
然而，作为一种无监督的子空间学习方法，LPP的分类能力较弱，并不是最有效的人脸识别方法。 - 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。 - 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。 - 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。