Statistical Relational Learning Algorithm: Difference between revisions
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Revision as of 06:42, 16 October 2014
A Statistical Relational Learning Algorithm is a relational learning algorithm that is a statistical learning algorithm.
- Context:
- It can handle Non-IID Samples.
- It can make use of a Statistical Relational Language.
- It can produce a Relational Predictive Model, such as a Relational Classifier or a Relational Estimator.
- It can make use of a Relational Feature, such as a Node-centric Feature (such as Node Aggregation Feature, or a Relation-centric Feature.
- Counter-Example(s):
- See: Markov Logic Network, Lifted Inference Algorithm.
References
2012
- http://en.wikipedia.org/wiki/Statistical_relational_learning
- Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with models of domains that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with reasoning (specifically probabilistic inference) and knowledge representation. Therefore, alternative terms that reflect the main foci of the field include statistical relational learning and reasoning (emphasizing the importance of reasoning) and first-order probabilistic languages (emphasizing the key properties of the languages with which models are represented).
- Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with models of domains that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
2007
- (Getoor & Taskar, 2007) ⇒ Lise Getoor, and Ben Taskar, editors. (2007). "Introduction to Statistical Relational Learning." MIT Press. ISBN:0262072882.
- QUOTE: Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data.