支持向量机是一种基于统计学习理论的新型机器学习方法，它可以被广泛地用于非线性系统建模。
Support vector machine (SVM) is a brand-new machine learning technique based on statistical learning theory. It is an ideal facility for modeling of various nonlinear systems.
在人工智能领域，神经网络是指通过将专门的计算设备连接成一个网络，模拟生物神经元，让使它们可以学习数据中潜在统计。
In artificial intelligence, a neural network simulates biological neurons by connecting specialized computing devices into a network, which allow them to learn the underlying statistics of the data.
很多职业顾问建议再学习一门可以使你的统计学的训练得以应用的学科。
Most career counselors recommend that you pursue a dual degree in a field where your statistical training might be applied.
数学运算、逻辑运算、统计也可以被使用在叠合后的资料及其属性，例如模拟选择及学习结果。
Arithmetic, logical, and statistical operations may be performed on the attributes, for example, when simulating alternatives and studying consequences.
支持向量机(SVM)是基于统计学习理论的一种智能学习方法，可以用来解决样本空间的高度非线性的模式识别等问题。
Support Vector Machine (SVM) is an intellectual learning method based on the statistics theory. The SVM can solve problems of complicated nonlinear pattern recognition of spatial samples.
在最大熵等统计机器学习模型当中，特征函数的选择可以说是对系统整体性能影响最大的部分。
The feature functions were reckoned as the most important part of the maximum entropy model which could affact the last result of system.
因为人工智能可以迅速筛查结构性数据，节约大量时间和金钱，又能深入阅读和学习非结构性数据、统计结果、文字和短语。
Because AI can save time and money by going through structured data quickly, as well as comprehensively reading and learning unstructured data, statistics, words, and phrases.
因为人工智慧可以迅速筛查结构性资料，节约大量时间和金钱，又能深入阅读和学习非结构性资料、统计结果、文字和短语。
Because AI can save time and money by going through structured data quickly, as well as comprehensively reading and learning unstructured data, statistics, words, and phrases.
将统计分析方法和面向属性的归纳方法结合起来，形成了一种应用面比较广的统计归纳学习方法，可以用于GIS属性数据挖掘。
A statistical inductive learning approach is proposed to investigate GIS attribute data mining. This approach integrates statistical analysis with attribute oriented induction method.
如果有一个适当的相似性度量方法或一个结合图表形式的向量表示空间，统计学习技术则可以被用上。
Other statistical learning techniques are applied to these if given an appropriate proximity measure or a vectorial representation space found by graph embeddings [79].
如果有一个适当的相似性度量方法或一个结合图表形式的向量表示空间，统计学习技术则可以被用上。
Other statistical learning techniques are applied to these if given an appropriate proximity measure or a vectorial representation space found by graph embeddings [79].
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