Rough set theory is one of the main subjects in the field of machine learning and data mining.
粗糙集理论是机器学习和数据挖掘领域的重要课题之一，其中属性约简算法是该理论实现应用的主要算法。
Abstract: Recently, the problem of Class-imbalance has become a hotspot in machine learning and data mining.
摘要：最近在机器学习和数据挖掘上，非平衡类问题成为了一个研究热点。
At present, manifold learning has become a hot issue in the field of pattern recognition, machine learning and data mining.
目前，流形学习已成为模式识别、机器学习和数据挖掘领域的研究热点问题。
Feature selection has abroad application in machine learning and data mining area, it is always applied as a primary pre-processing step.
属性选择通常作为一个主要的预处理步骤，在机器学习和数据挖掘领域有着广泛的应用。
Machine learning and data mining techniques are applied to acquire knowledge and build a concept reasoning network based on semantic dictionary and large training set.
在已有的英语语义词典及大量训练集的基础上，应用机器学习、数据挖掘等技术进行知识获取并最终形成若干个概念推理网。
Many real world problems deal with ordering objects instead of classifying objects, though most research in machine learning and data mining has been focused on the latter.
许多关于数据挖掘和机器学习的研究都集中于分类的研究，然而现实世界涉及到的不仅仅是分类问题，比如对象的排序问题。
At present, outlier data mining is a hotspot for the researchers of database, machine learning and statistics.
目前，离群挖掘正逐渐成为数据库、机器学习、统计学等领域研究人员的研究热点。
Data mining is an intercrossed subject, involving many fields such as machine learning, model reorganization, induction and deduction, statistics, database and high performance calculation.
数据挖掘是一门交叉性学科，涉及机器学习、模式识别、归纳推理、统计学、数据库、高性能计算等多个领域。
Support Vector Machine is a new method based on the idea of VC dimension and Statistical Learning Theory in data mining.
支持向量机是基于VC维和统计学习理论理念的数据挖掘中的一种新方法。
Data Mining is a new technology which appeared in recent years. It combines with machine learning, statistics, database and many other fields' technologies.
数据挖掘是近年来出现的一种综合了机器学习、统计学、数据库等众多领域的新技术。
Many existing data mining and machine learning techniques fail when training and test data have different distributions or feature spaces.
众所周知，当训练数据和测试数据的分布或特征空间不同时，许多数据挖掘和机器学习技术很有可能会失败。
And whereas machine learning, data mining, and predictive analysis are all narrowly scoped, advanced analytics is a broader scope that includes them all.
而机器学习、数据挖掘和预测分析都是狭义的范围，高级分析是一个更广泛的范围，包括它们所有。
His research interests include data mining, machine learning, and recommendation systems.
他的研究兴趣包括数据挖掘、机器学习以及推荐系统。
Currently, RST has been applied in many fields as artificial intelligence, machine learning, data mining, decision support and analysis, process control, pattern recognition, fault detection.
目前，粗糙集理论已被成功地应用在人工智能、机器学习、数据挖掘、决策支持与分析、过程控制、模式识别、故障检测等领域。
Stock price behavior data mining has aroused great concern in research of computer science, economy, machine learning and other fields.
股票价格行为数据挖掘激发了计算机科学、机器学习及其他领域研究的广泛关注。
His current research interests are in the areas of Data Mining, Artificial Intelligence, and Machine Learning.
目前，主要从事数据库、数据挖掘、人工智能等方面的研究工作。
For more effective meteorological data mining, this thesis introduces the quotient space granular computing theory, grey model, structural machine learning algorithm and so on.
为了更加有效地进行瓦斯数据挖掘，本文引入了商空间粒度计算理论、灰色模型、覆盖算法等。
The invention can be used in machine learning and the pattern recognition, as well as in the fields of voice recognition and data mining in addition to the image recognition.
本发明可用于机器学习和模式识别范畴内，除了图像识别以外，还可用于语音识别及数据挖掘等领域。
Researchers are getting more and more attentions to cluster analysis, since it involved in many fields such as statistics, data mining, machine learning and image processing, etc.
聚类分析涉及到统计学、数据挖掘、机器学习和图像处理等多个领域，人们对它研究热情日益高涨。
He states that "it's safe to regard machine learning, data mining, predictive analysis, and advanced analytics as more or less synonymous."
他说，“把机器学习、数据挖掘、预测分析和高级分析作为同义词是可以的。”
So it's important to change WEKA machine learning platform into a data mining research and application platform.
完善WEKA平台对数据挖掘的研究与应用具有重要意义。
This is the PDF of the book "Data. Mining. Practical. Machine. Learning. Tools. and. Techniques", a very good book for Data Mining. This is the 2nd edition in English.
《数据挖掘：实用机器学习技术(第二版)》(英文版)的PD F文档，非常好的数据挖掘书籍，其中关于Weka的介绍是亮点。
This is the PDF of the book "Data. Mining. Practical. Machine. Learning. Tools. and. Techniques", a very good book for Data Mining. This is the 2nd edition in English.
《数据挖掘：实用机器学习技术(第二版)》(英文版)的PD F文档，非常好的数据挖掘书籍，其中关于Weka的介绍是亮点。
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