The analytical work involves applying statistical inference and machine learning techniques.
其分析工作涉及到统计推断及机器学习技术的应用。
One area that's going to get a lot of attention is combining machine learning with causal inference.
未来有一个领域将会得到很多的关注，那就是将机器学习与因果推理相结合。
Emphasizes questions of inductive learning and inference, and the representation of knowledge.
著重在归纳学习、推断及知识表现的问题探讨。
This dissertation focuses on efficient exact inference on belief networks, learning belief networks from data, and classification using belief networks.
本论文详细研究了信度网精确推理、信度网学习和信度网分类有关内容。
Decision tree learning is one of the widely used and practical methods for inductive inference.
决策树学习是应用最广泛的归纳推理算法之一。
A fuzzy neural network prediction control model is stated by using the logic inference performance of fuzzy control and the learning ability of neural network.
应用模糊控制的逻辑推理性能，借助神经网络的学习能力，提出了一种模糊神经网络预测控制模型。
Recognition of patterns and inference skills lie at the core of human learning.
模式识别和推理技能是人类学习能力的核心所在。
A fuzzy Q learning algorithm is proposed in this dissertation, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.
首先，提出一种模糊Q学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。
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 compensation fuzzy neural network (CFNN) with fast learning algorithm and compensation fuzzy inference is introduced in this paper.
本文介绍了一种具有快速学习算法、能够执行补偿模糊推理的补偿模糊神经网络。
The inference algorithm is the basis of learning and application in belief network.
推理算法是信度网学习和应用的基础。
The membership functions and the inference rules in the controller are modified using the learning functions of neural network so that the adaptability of the controller is further enhanced.
利用神经网络的学习功能对控制器的隶属度函数及推理规则进行修正，以提高其自适应能力。
The parameters of me fuzzy control rules of me controller can be learned by the learning slgorithm of the neural netowrk. and the inference process can be realized by the network.
应用单层神经网络可以学习多变量模糊控制规则中的未知参数．还可由它来实现多变量模糊推理过程。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
贝叶斯学习理论使用概率去表示所有形式的不确定性，通过概率规则来实现学习和推理过程。
Dynamic Bayesian Network (DBN), because of extensibility, powerful description, inference and learning abilities for the time series, being used in the speech recognition.
动态贝叶斯网络(DBN)，以其扩展性和对时间序列的强大描述、推导和学习能力，逐渐被应用于连续语音识别中。
This text USES some theory researching method, such as logical inference, documentary method and so on to research the cause of difficulties in learning physics and gives teaching countermeasures.
本文利用逻辑推理、文献法等理论研究方法，对高中生学习物理困难的原因及相应教学对策进行了研究。
Transductive inference based on support vector machine is a relatively new research region in statistical learning theory.
基于支持向量机的直推式学习是统计学习理论中一个较新的研究领域。
FNN efficiently maps the complex non-linear relationship between data by drill and rebound methods for its automatic learning, generation and fuzzy logic inference.
由于模糊神经网络具有很强的自学习、泛化和模糊逻辑推理功能，它可以有效地映射出钻芯、回弹数据间复杂的非线性关系。
Category learning is increasingly concerned in the last decade. There are two forms of category learning, classification learning and inference learning.
类别学习在近十年受到研究者的极大关注，类别学习有两种形式，分别为分类学习和推理学习。
Medical Diagnosis; Machine Learning; Back-Propagation Neural Network; Adaptive Neural Fuzzy Inference System.
医学诊断；机器学习；倒传递网路；适应性类神经模糊推论系统。
The system's conformation, function, self-learning ability, setting up of a knowledge bank and the formation of an inference mechanism are being explained.
介绍了该系统的结构、功能、自学习能力和知识库的建立与推理机的实现。
While the article proposes the improved inference and self-learning method of fuzzy rule.
并提出了模糊规则的改进推理和自学习方法。
This adaptive fuzzy controller is based on fuzzy inference rules self-learning without needing so much expert control rules, which solves the problem of acquiring MIMO fuzzy inference rules.
这种自适应模糊控制器基于模糊推理规则自学习和自调整的控制算法，无需知道太多的专家控制规则，因此解决了制冷系统MIMO模糊推理规则难以获取的问题。
With the neural network based approach, human knowledge and machine learning are effectively combined together in the semantic inference.
通过这种方法，我们能够准确地提取多媒体传感器网络中的音频高层语义信息。
What's the Difference Between Deep Learning Training and Inference?
深度学习训练与推理有何不同？
What's the Difference Between Deep Learning Training and Inference?
深度学习训练与推理有何不同？
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