463
个编辑
更改
→Relation to data mining
/ 参考
/ 参考
=== Relation to data mining ===
=== 与数据挖掘 Data mining的关系Relation to data mining ===
Machine learning and [[data mining]] often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on ''known'' properties learned from the training data, [[data mining]] focuses on the [[discovery (observation)|discovery]] of (previously) ''unknown'' properties in the data (this is the analysis step of [[knowledge discovery]] in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, [[ECML PKDD]] being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to ''reproduce known'' knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously ''unknown'' knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Machine learning and [[data mining]] often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on ''known'' properties learned from the training data, [[data mining]] focuses on the [[discovery (observation)|discovery]] of (previously) ''unknown'' properties in the data (this is the analysis step of [[knowledge discovery]] in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, [[ECML PKDD]] being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to ''reproduce known'' knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously ''unknown'' knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
机器学习和数据挖掘虽然在使用方法上有些相似并且有很大的重叠，但是机器学习的重点是预测，基于从训练数据中学到的已知属性，而数据挖掘的重点则是发现数据中（以前）未知的属性（这是'''数据库中知识发现 Knowledge Discovery in Database, KDD'''的基本分析步骤），也就是说数据挖掘虽然使用了许多机器学习方法，但二者的目标不同; 另一方面，机器学习也使用数据挖掘方法作为“无监督学习”或作为提高学习者准确性的预处理步骤。这两个研究领域之间的混淆（这两个领域通常有各自单独的会议和单独的期刊，ECML PKDD是一个例外）来自他们工作的基本假设: 在机器学习中，算法性能通常是根据再现已知知识的能力来评估，而在知识发现和数据挖掘中，其关键任务是发现以前未知的知识，因此在对已知知识进行评价时，其他监督方法很容易超过未知（无监督）方法，而在典型的知识发现任务中，由于缺乏训练数据，无法使用有监督的学习算法。
=== Relation to optimization ===
=== Relation to optimization ===