Many robots are equipped with high-tech sensors and complex learning algorithms to avoid injuring humans as they work side by side.
许多机器人都配备了高科技传感器和复杂的学习算法，避免在与人类一起工作时伤害到人类。
The types of learning algorithms fall along several classifications.
学习算法分为几个不同的类别。
Document Indexing is a crucial step for learning algorithms and classifying systems.
文档索引对于学习算法和分类系统都是至关重要的一步。
For now, be aware that even apparently simple learning algorithms can accomplish a great deal.
现在我们只需要知道，即使是简单明确的算法也可以做很多事情。
Each company USES huge pools of data to help train sophisticated machine learning algorithms.
每一家公司都在利用海量的数据，帮助训练复杂的机器学习算法。
Like machine learning algorithms themselves, there is no perfect model, just a good enough model.
就像机器学习算法自身一样，没有完美的模型，只有足够好的模型。
The self-learning algorithms of the task class knowledge need model and user model are put forward.
还给出了任务类知识需求模板和用户模型的自学习算法。
Machine learning algorithms are being developed that allow robots and computers to learn autonomously.
机器学习算法正在被开发出来，它允许机器人和计算机进行自主学习。
Decision tree simplification is a significant branch in the study of decision-tree learning algorithms.
决策树简化是决策树学习算法中的一个重要分支。
In those lazy learning algorithms most extensively used is nearest neighbor classification (NN) algorithm.
其中消极学习型中应用最广泛的是最近邻分类算法。
Nevertheless, learning algorithms can give insight into the relative difficulty of learning in different environments.
然而，学习算法却可以在那些难以学习的环境中赋予其洞察力。
Genetic algorithms (GA) are optimization and machine learning algorithms inspired by processes of natural evolution.
遗传算法是由受生物进化过程启发而形成的进行优化和机器学习的算法。
The authors briefly review the theory of cost-sensitive learning, and the existing cost-sensitive learning algorithms.
简要地回顾了代价敏感学习的理论和现有的代价敏感学习算法。
This paper gives a comprehensive overview about agent learning algorithms used in economic complexity system research.
对经济复杂性系统研究文献中常见的主体学习算法进行了一个全面的梳理。
It supports directed and undirected models, discrete and continuous variables, various inference and learning algorithms.
它支持有向或无向的模型，离散或连续的变量，各种推论及学习算法。
The thesis mainly focuses on the dynamic scheduling method based on the averaged rewards reinforcement learning algorithms.
论文主要研究了基于平均型强化学习算法的动态调度方法。
Designs of the iterative learning algorithms, the most important problems in the ILC, are also studied in this dissertation.
迭代学习算法设计一直是迭代学习控制研究的重点，本文从一些新的视角做了探讨。
It will use computer vision, sensors and deep learning algorithms to keep track of what customers are picking up off the shelves.
系统将会使用机械视觉、感应器和深度学习算法来跟踪记录顾客从货架上选取的商品。
It is rational to adopt the average reward reinforcement learning algorithms for solving the absorbing goal states cyclical tasks.
对于有吸收目标状态的循环任务，比较合理的方法是采用基于平均报酬模型的强化学习。
First, we summarize some amS learning algorithms on Gaussian or finite mixture based on the Bayesian Ying-Yang (BYY) harmony learning principle.
首先，我们综述了基于贝叶斯阴阳机和谐学习原则的自动模型选择学习算法。
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。
Molnar's other research in the field includes using machine-learning algorithms in an effort to further understand how humans' listen 'to dog barks.
molnar在这个领域的其他研究包括，使用人工智能来更进一步了解人类是如何“听”狗叫的。
The proposed learning algorithm can reduce the network sensitivity and keep the same convergence speed comparing with traditional learning algorithms.
次种学习算法与传统学习算法相比，可降低网络的灵敏度，但学习收敛速度基本相同。
Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation.
模糊c -均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。
The robot used this data as an input for its machine learning algorithms and created a map between its facial expressions and the movements of its muscle motors.
该机器人用这些数据作为其机器学习算法的输入数据，产生一个脸部表情和相应肌肉马达运动之间的信息存储分布图。
SVM is a kind of general learning algorithms, which has been widely used in pattern recognition, regression estimation, function approximation, density estimation, etc.
支撑矢量机是一种普适的算法，已经广泛地用于模式识别、回归估计、函数逼近、密度估计等方面。
The continuous attribute problems are often encountered in the real world, but many outstanding inductive learning algorithms are mainly based on a discrete feature space.
实际问题中经常涉及连续的数值属性，然而许多归纳学习算法却是针对离散属性空间的。
To develop effective learning algorithms for fast and accurate continuous prediction using Electroencephalogram (EEG) signal is a key issue in BrainComputer Interface (BCI).
设计有效的学习算法快速准确地对脑电信号进行连续预测是脑机接口研究的关键之一。
To develop effective learning algorithms for fast and accurate continuous prediction using Electroencephalogram (EEG) signal is a key issue in BrainComputer Interface (BCI).
设计有效的学习算法快速准确地对脑电信号进行连续预测是脑机接口研究的关键之一。
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