Other feedforward models and supervised learning models.
其它前馈型网络模型和监督学习模型;
Lattice machine is a novel approach to supervised learning.
格机是一种新颖的有监督学习方法。
If you want to use some supervised learning algorithm, you need labeled data.
如果你想使用有监督的学习算法,你需要标记数据。
What are the differences between supervised learning and reinforcement learning?
监督学习与强化学习的区别是什么?
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。
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。
A semi-supervised learning system was proposed based on ART (adaptive resonance theory).
根据自适应谐振理论提出了半监督学习自适应谐振理论系统。
A 3d expression generating method based on morphing and supervised learning is introduced.
提出一种基于变形和监督式学习的三维表情生成方法。
Supervised learning is the most common technique for training neural networks and decision trees.
监督学习是训练神经网络和决策树的最常见技术。
The control system works at distal supervised learning control mode or extreme control mode.
控制系统有远程控制与极值控制两种模式。
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory.
这个课程的重点将放在与统计学习理论架构中有关的监督式学习问题。
In this paper, we discuss the problem of using semi-supervised learning method to do video semantic annotation.
本文讨论了利用半监督学习方法进行视频语义标注的问题。
Supervised learning with the use of regression and classification networks with sparse data sets will be explored.
也将在课程中以带有稀疏值理论的分类神经网路与回归的使用来探讨监督式学习。
Semi-supervised learning - Combines both labeled and unlabeled examples to generate an appropriate function or classifier.
半分类学习-将标签与非标签用例劫后生成一个合适的函数或分类器。
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。
Reinforcement learning has the ability to learn from experience as opposed to supervised learning which learns from examples.
与监督学习从范例中学习的方式不同,强化学习不需要先验知识,而是具有从经验中学习的能力。
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。
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。
That's a very active area of machine-learning research, but it's not a solved problem to the extent that supervised learning is.
这是机器学习研究的一个非常活跃的领域,但目前研究的进展与监督式学习还是不能比拟的。
This paper reviews the problem of semi-supervised learning the basic idea of the status quo, Summarized the current study difficult.
该文综述了半监督:学习问题的基本思想、研究现状,简述目前的研究困难。
However, traditional supervised learning techniques typically require a large number of labeled examples to learn an accurate classifier.
然而,传统的监督学习算法需要标记大量的训练样本来建立满意的分类器。
Presents a method of training a feedforward neural network using supervised learning scheme to balance an inverted pendulum and cart system.
采用平衡的倒摆小车所记录下来的数据,经处理后用有师学习方法来训练前馈神经网络。
The technical term for this is supervised learning, and I think we just haven't figured out the right ideas yet for the other types of learning.
用术语来说,这种方式叫监督学习,我认为到目前为止,我们还没有找到其他学习类型的正确思路。
The ANN can derive a continuous-spread stability index to indicate the relative stability degree by means of a semi-supervised learning algorithm.
使用一个半监督学习算法,ANN可产生一个能够指示相对稳定度的连续分布的暂态稳定指标。
At present, most of video semantic annotation methods are based on statistic theory. The methods use supervised learning method to do semantic label.
目前已有的视频语义标注方法多是基于统计学理论,采用全监督学习方法进行语义标注工作。
Common examples of supervised learning include classifying E-mail messages as spam, labeling Web pages according to their genre, and recognizing handwriting.
监管学习的常见例子包括将电子邮件消息分类为垃圾邮件,根据类别标记网页,以及识别手写输入。
Common examples of supervised learning include classifying E-mail messages as spam, labeling Web pages according to their genre, and recognizing handwriting.
监管学习的常见例子包括将电子邮件消息分类为垃圾邮件,根据类别标记网页,以及识别手写输入。
应用推荐