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。
Many existing data mining and machine learning techniques fail when training and test data have different distributions or feature spaces.
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。
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.
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。
And whereas machine learning, data mining, and predictive analysis are all narrowly scoped, advanced analytics is a broader scope that includes them all.
数据挖掘是个新兴的研究领域，涉及到统计学、数据库、机器学习等众多学科，正以其强大的功能和广泛的应用受到高度的关注。
DataMing is a new study realm, coming down to many subjects such as statistics, database, machine learning and so on, it was paid high attention for its strong functions and broad application.
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。
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.
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。
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.
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。”
He states that "it's safe to regard machine learning, data mining, predictive analysis, and advanced analytics as more or less synonymous."
本文选择蛋白质二级结构数据为主要的研究对象，应用数据挖掘技术和机器学习中的动态规划理论进行蛋白质结构分类。
Protein second structure data is chosen as main study object, and data mining and dynamic programming are applied to protein structure classification.
本文选择蛋白质二级结构数据为主要的研究对象，应用数据挖掘技术和机器学习中的动态规划理论进行蛋白质结构分类。
Protein second structure data is chosen as main study object, and data mining and dynamic programming are applied to protein structure classification.
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