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。
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.
该文根据自相关函数与谱密度函数之间的对应关系，提出了一种新的基于自相关函数的决策树归纳学习算法。
According to the relationship between auto correlation function and its spectral density, a new type of decision tree method based on signal analysis theory is proposed in this paper.
到目前为止，众多有关机器学习的文章中一个重要的主题是利用算法对训练数据进行总结归纳，而不是简单的记忆。
So far, a major theme in these machine learning articles has been having algorithms generalize from the training data rather than simply memorizing it.
基于ID 3算法的决策树归纳学习是归纳学习的一个重要分支，可用于知识的自动获取过程。
Induction learning of decision tree based on ID3 algorithm is an important branch of inductive learning now, which can be used to automatic acquisition of knowledge.
决策树学习是应用最广泛的归纳推理算法之一。
Decision tree learning is one of the widely used and practical methods for inductive inference.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
属性选择是机器学习的核心问题之一，它关系到归纳算法的复杂性和学习性能。
The selection of attributes, which involves the complexity and performance of induction algorithms, is.
为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。
The basic statistical learning theory (SLT) and its corresponding algorithms, support vector machines (SVMs), are surveyed, and especially, its latest research results are summarized and studied.
模拟结果表明利用该算法训练的模糊层次神经网络具有较好的非逻辑归纳能力和特征抽取能力，并且学习速度也大大加快。
The simulation result is that the Fuzzy forward neural networks which is trained by this algorithm have good non-logic generalization and feature extraction ability, as well as fast learning speed.
针对传统归纳学习的困难，提出一种新的逆演绎的学习算法。
This paper proposes a new algorithm called LAID(learning algorithm as inverse deduction) considering the difficulty of traditional induction learning.
总结归纳了机器学习方法在目前生物信息学的应用，并对支撑向量机（SVM）算法的基本原理做了阐述；
Secondly, summarize the application of the machine learning methods in the bioinformatics and expatiate on the rationale of the Support Vector Machine (SVM).
在对归纳学习理论深入研究的基础上，将规则学习算法应用到入侵检测建模中。
Based on the in-deep research on inductive learning theory, a rule learning algorithm is applied in building the intrusion detection model.
介绍了归纳学习中的决策树学习算法如id3、C4.5和特征子集选择问题。
And it introduces some algorithms of decision tree learning such as ID3, C4.5 and feature subset selection of Inductive learning.
为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。
Support vector machine is a new machine learning algorithm, based theoretically on statistic learning theory created by Vapnik.
为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。
Support vector machine is a new learning machine, and it is based on the statistics learning theory.
为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。
Support vector machine is a new learning machine, and it is based on the statistics learning theory.
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