文本挖掘中机器学习概念的综合研究

K. Surya, R. Nithin, S. Prasanna, R. Venkatesan
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引用次数: 4

摘要

机器学习的目的是利用过去的经验或示例数据来解决给定的问题。许多机器学习应用现在已经在使用了。随着更多的数据变得可访问,更多有抱负的问题可以得到处理。在这里。在这种情况下,我们将详细了解文本挖掘作为一个多维领域,它涉及密切相关的区域或部分,如1。检索信息;机器学习概念,简称ML, 3。统计,4。最后是计算语言学,特别要提一下,数据挖掘。通过使用样本数据或先前获得的经验,机器学习被纳入计算机以增强或改善性能决定性因素。在这种情况下,我们已经详细说明了一个模型到某种程度的约束,学习是处理的一个主要内容,以增强参数的形式使用训练或样本数据或以前获得的经验。这可能是为了从给定的数据中获得知识,或者利用未来变化的影响,或者两者兼而有之。这些学习技术也帮助我们解决各种错误,包括视觉、语音识别和机器人。我们以任务和程序的预处理为例,主要分析了分类、聚类、信息提取和可视化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comprehensive study on machine learning concepts for text mining
The aim of machine learning is to solve a given problem using past experience or example data. Many machine learning applications are using now-a-days already. More aspiring problems can be handled as more data become accessible. Here. in this context we learn in detail about text mining as a multi-dimensional field which involves the closely linked areas or sections like 1. Retrieving information, 2. Machine learning concepts shortly termed as ML, 3. Statistics, 4. And finally Computational linguistics and specifically to be mentioned, data mining. With the use of sample data or previously gained experience, machine learning is included into computers to enhance or improve a performance decisive factor. In this context we have detailed a model up to some level of constraints, and learning is the processing of a main content to enhance the parameter of the form using the training or sample data or previously gained experience. This may be designed to gain knowledge from the given data, or use the effect for changes in the future, or both. These learning techniques also helps us to make solutions to various bugs which includes vision, speech recognition, and robotics. We take the example of the main analysis of preprocessing of tasks and procedures, then classification, then clustering, information extraction and finally visualization.
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