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第667行： − + − − − − Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.<ref>{{Cite journal|last=Bassel|first=George W.|last2=Glaab|first2=Enrico|last3=Marquez|first3=Julietta|last4=Holdsworth|first4=Michael J.|last5=Bacardit|first5=Jaume|date=2011-09-01|title=Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets|journal=The Plant Cell|language=en|volume=23|issue=9|pages=3101–3116|doi=10.1105/tpc.111.088153|issn=1532-298X|pmc=3203449|pmid=21896882}}</ref> Rule-based machine learning approaches include [[learning classifier system]]s, association rule learning, and [[artificial immune system]]s. − − Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. − − 基于规则的机器学习是任何机器学习方法的通用术语，这些机器学习方法识别、学习或发展“规则”来存储、操作或应用知识。基于规则的机器学习算法这一定义的特点是识别和利用一组共同表示系统捕获的知识的关系规则。这与其他机器学习算法不同，后者往往只识别一个单一的模型，这个模型可以普遍应用于任何实例，以便进行预测。基于规则的机器学习方法包括'''学习分类器系统 Learning Classifier System'''、关联规则学习和'''人工免疫系统 Artificial Immune System'''。 − − 第680行：
第673行： − 基于强规则的原理，Rakesh Agrawal、 Tomasz imieli ski 和 Arun Swami 引入了关联规则这一概念，用于在超市销售点（POS）系统记录的大规模交易数据中发现产品之间的规则。例如，在超市的销售数据中发现的规则表明，如果某位顾客同时购买洋葱和土豆，那么他也很可能会购买汉堡肉。这些信息可以作为市场决策的依据，如促销价格或产品植入。除了市场篮子分析之外，关联规则还应用于 '''Web 使用挖掘 Web Usage Mining'''、'''入侵检测 Intrusion Detection'''、连续生产和'''生物信息学 Bioinformatics'''等应用领域。与序列挖掘相比，关联规则学习通常不考虑事务内或事务之间的先后顺序。+ − − − 下面是几种常见的基于规则的机器学习算法： − − − Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a [[genetic algorithm]], with a learning component, performing either [[supervised learning]], [[reinforcement learning]], or [[unsupervised learning]]. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a [[piecewise]] manner in order to make predictions.<ref>{{Cite journal|last=Urbanowicz|first=Ryan J.|last2=Moore|first2=Jason H.|date=2009-09-22|title=Learning Classifier Systems: A Complete Introduction, Review, and Roadmap|journal=Journal of Artificial Evolution and Applications|language=en|volume=2009|pages=1–25|doi=10.1155/2009/736398|issn=1687-6229|doi-access=free}}</ref> − − Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. − '''学习分类器系统 Learning Classifier Systems，LCS'''是一系列基于规则的机器学习算法，它结合了一个发现组件，通常是一个遗传算法和一个学习组件，执行监督式学习、强化学习或非监督式学习。他们试图给出一组与上下文相关的规则，而这些规则以分段的方式共同储存和应用知识，以便进行预测。 + + + + 第699行：
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→关联规则 Association rules
在'''发展型机器人 Developmental robotics'''学习中，机器人学习算法能够产生自己的学习经验序列，也称为课程，通过自我引导的探索来与人类社会进行互动，累积获得新技能。这些机器人在学习的过程中会使用诸如主动学习、成熟、协同运动和模仿等引导机制。
在'''发展型机器人 Developmental robotics'''学习中，机器人学习算法能够产生自己的学习经验序列，也称为课程，通过自我引导的探索来与人类社会进行互动，累积获得新技能。这些机器人在学习的过程中会使用诸如主动学习、成熟、协同运动和模仿等引导机制。
==== 基于规则的机器学习算法 ====
[https://en.wikipedia.org/wiki/Rule-based_machine_learning 基于规则的机器学习]是任何机器学习方法的通用术语，它通过识别、学习或演化“规则”来存储、操作或应用知识。基于规则的机器学习者的定义特征是识别和使用一组关系规则，这些规则共同表示系统获取的知识。这与其他机器学习者不同，这些机器学习者通常会识别出一个可以普遍应用于任何实例的奇异模型，以便进行预测<ref>{{Cite journal|last=Bassel|first=George W.|last2=Glaab|first2=Enrico|last3=Marquez|first3=Julietta|last4=Holdsworth|first4=Michael J.|last5=Bacardit|first5=Jaume|date=2011-09-01|title=Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets|url=http://www.plantcell.org/content/23/9/3101|journal=The Plant Cell|language=en|volume=23|issue=9|pages=3101–3116|doi:10.1105/tpc.111.088153|issn:1532-298X|pmc:3203449|pmid:21896882}}</ref> 。基于规则的机器学习方法包括[https://en.wikipedia.org/wiki/Learning_classifier_system 学习分类器系统]、[https://en.wikipedia.org/wiki/Association_rule_learning 关联规则学习]和[https://en.wikipedia.org/wiki/Artificial_immune_system 人工免疫系统]。
==== 关联规则 Association rules ====
==== 关联规则 Association rules ====
{{Main|Association rule learning}}{{See also|Inductive logic programming}}
{{Main|Association rule learning}}{{See also|Inductive logic programming}}
Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".
Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".
'''关联规则学习 Association Rule Learning'''是一种'''基于规则的机器学习 Rule-based machine learning'''方法，用于发现大型数据库中变量之间的关系。它旨在利用某种“有趣度”的度量，识别在数据库中发现的强大规则。
'''关联规则学习 Association Rule Learning'''是一种'''基于规则的机器学习 Rule-based machine learning'''方法，用于发现大型数据库中变量之间的关系。它旨在利用某种“有趣度”的度量，识别在数据库中新发现的强大规则。
Based on the concept of strong rules, [[Rakesh Agrawal (computer scientist)|Rakesh Agrawal]], [[Tomasz Imieliński]] and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by [[point-of-sale]] (POS) systems in supermarkets.<ref name="mining">{{Cite book | last1 = Agrawal | first1 = R. | last2 = Imieliński | first2 = T. | last3 = Swami | first3 = A. | doi = 10.1145/170035.170072 | chapter = Mining association rules between sets of items in large databases | title = Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93 | pages = 207 | year = 1993 | isbn = 978-0897915922 | pmid = | pmc = | citeseerx = 10.1.1.40.6984 }}</ref> For example, the rule <math>\{\mathrm{onions, potatoes}\} \Rightarrow \{\mathrm{burger}\}</math> found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional [[pricing]] or [[product placement]]s. In addition to [[market basket analysis]], association rules are employed today in application areas including [[Web usage mining]], [[intrusion detection]], [[continuous production]], and [[bioinformatics]]. In contrast with [[sequence mining]], association rule learning typically does not consider the order of items either within a transaction or across transactions.
Based on the concept of strong rules, [[Rakesh Agrawal (computer scientist)|Rakesh Agrawal]], [[Tomasz Imieliński]] and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by [[point-of-sale]] (POS) systems in supermarkets.<ref name="mining">{{Cite book | last1 = Agrawal | first1 = R. | last2 = Imieliński | first2 = T. | last3 = Swami | first3 = A. | doi = 10.1145/170035.170072 | chapter = Mining association rules between sets of items in large databases | title = Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93 | pages = 207 | year = 1993 | isbn = 978-0897915922 | pmid = | pmc = | citeseerx = 10.1.1.40.6984 }}</ref> For example, the rule <math>\{\mathrm{onions, potatoes}\} \Rightarrow \{\mathrm{burger}\}</math> found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional [[pricing]] or [[product placement]]s. In addition to [[market basket analysis]], association rules are employed today in application areas including [[Web usage mining]], [[intrusion detection]], [[continuous production]], and [[bioinformatics]]. In contrast with [[sequence mining]], association rule learning typically does not consider the order of items either within a transaction or across transactions.
Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule <math>\{\mathrm{onions, potatoes}\} \Rightarrow \{\mathrm{burger}\}</math> found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.
Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule <math>\{\mathrm{onions, potatoes}\} \Rightarrow \{\mathrm{burger}\}</math> found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.
在基于强规则的原理中，Rakesh Agrawal、 Tomasz imieli ski 和 Arun Swami 引入了关联规则这一概念，用于在超市销售点（POS）系统记录的大规模交易数据中发现产品之间的规则。例如，在超市的销售数据中发现的规则表明，如果某位顾客同时购买洋葱和土豆，那么他也很可能会购买汉堡肉。这些信息可以作为市场决策的依据，如促销价格或产品植入。除了市场篮子分析之外，关联规则还应用于 '''Web 使用挖掘 Web Usage Mining'''、'''入侵检测 Intrusion Detection'''、连续生产和'''生物信息学 Bioinformatics'''等应用领域。与序列挖掘相比，关联规则学习通常不考虑事务内或事务之间的先后顺序。
====学习分类器====
:''主文章：[https://en.wikipedia.org/wiki/Learning_classifier_system 学习分类器系统]''
'''学习分类器系统 Learning Classifier Systems，LCS'''是一组[https://en.wikipedia.org/wiki/Rule-based_machine_learning 基于规则的机器学习]算法，它将发现组件(通常是[https://en.wikipedia.org/wiki/Genetic_algorithm 遗传算法])与学习组件(执行有[https://en.wikipedia.org/wiki/Supervised_learning 监督学习]、[https://en.wikipedia.org/wiki/Reinforcement_learning 强化学习]或[https://en.wikipedia.org/wiki/Unsupervised_learning 无监督学习])结合起来。他们试图找出一套与情境相关的规则，这些规则以一种[https://en.wikipedia.org/wiki/Piecewise 分段]的方式，集体存储和应用知识，以便进行预测<ref>{{Cite journal|last=Urbanowicz|first=Ryan J.|last2=Moore|first2=Jason H.|date=2009-09-22|title=Learning Classifier Systems: A Complete Introduction, Review, and Roadmap|url=http://www.hindawi.com/archive/2009/736398/|journal=Journal of Artificial Evolution and Applications|language=en|volume=2009|pages=1–25|issn:1687-6229}}</ref>。
==== 归纳逻辑规划 ====
Inductive logic programming (ILP) is an approach to rule-learning using [[logic programming]] as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that [[Entailment|entails]] all positive and no negative examples. [[Inductive programming]] is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as [[Functional programming|functional programs]].
Inductive logic programming (ILP) is an approach to rule-learning using [[logic programming]] as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that [[Entailment|entails]] all positive and no negative examples. [[Inductive programming]] is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as [[Functional programming|functional programs]].
'''归纳逻辑规划 Inductive Logic Programming，ILP'''是一种用逻辑规划作为输入示例、背景知识和假设的统一表示的规则学习方法。如果将已知的背景知识进行编码，并将一组示例表示为事实的逻辑数据库，ILP 系统将推导出一个假设的逻辑程序，其中包含所有正面和负面的样例。归纳编程是一个与其相关的领域，它考虑用任何一种编程语言来表示假设（不仅仅是逻辑编程），比如'''函数编程 Functional programs'''。
'''归纳逻辑规划 Inductive Logic Programming，ILP'''是一种用逻辑规划作为输入示例、背景知识和假设的统一表示的规则学习方法。如果将已知的背景知识进行编码，并将一组示例表示为事实的逻辑数据库，ILP 系统将推导出一个假设的逻辑程序，其中包含所有正面和负面的样例。归纳编程是一个与其相关的领域，它考虑用任何一种编程语言来表示假设（不仅仅是逻辑编程），比如'''函数编程 Functional programs'''。