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Statistical Relational Learning
Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Artificial Intelligence
Artificial intelligence (AI) is the capability of computer, computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to machine perception, perceive their environment and use machine learning, learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon (company), Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Amazon Alexa, Alexa); autonomous vehicles (e.g., Waymo); Generative artificial intelligence, generative and Computational creativity, creative tools (e.g., ChatGPT and AI art); and Superintelligence, superhuman play and analysis in strategy games (e.g., ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Relational Dependency Network
Relational dependency networks (RDNs) are graphical models which extend dependency networks to account for relational data. Relational data is data organized into one or more tables, which are cross-related through standard fields. A relational database is a canonical example of a system that serves to maintain relational data. A relational dependency network can be used to characterize the knowledge contained in a database. Introduction Relational Dependency Networks (or RDNs) aims to get the joint probability distribution over the variables of a dataset represented in the relational domain. They are based on Dependency Networks (or DNs) and extend them to the relational setting. RDNs have efficient learning methods where an RDN can learn the parameters independently, with the conditional probability distributions estimated separately. Since there may be some inconsistencies due to the independent learning method, RDNs use Gibbs sampling to recover joint distribution, like DN ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Relational Bayesian Network
Relational may refer to: Business * Relational capital, the value inherent in a company's relationships with its customers, vendors, and other important constituencies * Relational contract, a contract whose effect is based upon a relationship of trust between the parties * Relational goods, goods that cannot be enjoyed alone * Relational Investors, an activist investment fund based in San Diego, California Computing * Relational calculus, part of the relational model for databases that provides a declarative way to specify database queries * Relational database, a database that has a collection of tables of data items, all of which is formally described and organized according to the relational model ** Relational classification, the procedure of performing classification in relational databases ** Relational data mining, the data mining technique for relational databases * Relational concept, a set of mathematically defined tuples in tuple relational calculus * Relational model, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Recursive Random Field
Recursion occurs when the definition of a concept or process depends on a simpler or previous version of itself. Recursion is used in a variety of disciplines ranging from linguistics to logic. The most common application of recursion is in mathematics and computer science, where a function being defined is applied within its own definition. While this apparently defines an infinite number of instances (function values), it is often done in such a way that no infinite loop or infinite chain of references can occur. A process that exhibits recursion is ''recursive''. Video feedback displays recursive images, as does an infinity mirror. Formal definitions In mathematics and computer science, a class of objects or methods exhibits recursive behavior when it can be defined by two properties: * A simple ''base case'' (or cases) — a terminating scenario that does not use recursion to produce an answer * A ''recursive step'' — a set of rules that reduces all successive cases t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Probabilistic Soft Logic
Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment. PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge. More specifically, PSL uses "soft" logic as its logical component and Markov random fields as its statistical model. PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the maximum a posteriori (MAP) state). The "softening" of the logical formulas makes inference a polynomial time operation rather than an NP-hard operation. Description The SRL community has introduced multiple approaches that combine graphical models and fir ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Probabilistic Logic Program
Probabilistic logic programming is a programming paradigm that combines logic programming with probabilities. Most approaches to probabilistic logic programming are based on the ''distribution semantics,'' which splits a program into a set of probabilistic facts and a logic program. It defines a probability distribution on interpretations of the Herbrand universe of the program. Languages Most approaches to probabilistic logic programming are based on the ''distribution semantics,'' which underlies many languages such as Probabilistic Horn Abduction, PRISM, Independent Choice Logic , probabilistic Datalog, Logic Programs with Annotated Disjunctions, ProbLog, P-log, and CP-logic. While the number of languages is large, many share a common approach so that there are transformations with linear complexity that can translate one language into another. Semantics Under the distribution semantics, a probabilistic logic program is interpreted as a set of independent probabilistic fac ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Markov Logic Network
A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, defining probability distributions on possible worlds on any given domain. History In 2002, Ben Taskar, Pieter Abbeel and Daphne Koller introduced relational Markov networks as templates to specify Markov networks abstractly and without reference to a specific domain. Work on Markov logic networks began in 2003 by Pedro Domingos and Matt Richardson. Markov logic networks is a popular formalism for statistical relational learning. Syntax A Markov logic network consists of a collection of formulas from first-order logic, to each of which is assigned a real number, the weight. The underlying idea is that an interpretation is more likely if it satisfies formulas with positive weights and less likely if it satisfies formulas with negative weights. For instance, the following Markov logic network codifies how smokers are more likely to be friends with other sm ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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BLOG Model
A blog (a Clipping (morphology), truncation of "weblog") is an informational website consisting of discrete, often informal diary-style text entries also known as posts. Posts are typically displayed in Reverse chronology, reverse chronological order so that the most recent post appears first, at the top of the web page. In the 2000s, blogs were often the work of a single individual, occasionally of a small group, and often covered a single subject or topic. In the 2010s, multi-author blogs (MABs) emerged, featuring the writing of multiple authors and sometimes professionally Editing, edited. MABs from newspapers, other News media, media outlets, universities, think tanks, advocacy groups, and similar institutions account for an increasing quantity of blog Web traffic, traffic. The rise of Twitter and other "microblogging" systems helps integrate MABs and single-author blogs into the news media. ''Blog'' can also be used as a verb, meaning ''to maintain or add content to a blog ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Bayesian Logic Program
Thomas Bayes ( ; c. 1701 – 1761) was an English statistician, philosopher, and Presbyterian minister. Bayesian ( or ) may be either any of a range of concepts and approaches that relate to statistical methods based on Bayes' theorem, or a follower of these methods. Bayes * * * * * * * * * * * – sometimes called ''Bayes' rule'' or ''Bayesian updating'' * * * * * * Bayesian *''Bayesian Thomas Bayes ( ; c. 1701 – 1761) was an English statistician, philosopher, and Presbyterian minister. Bayesian ( or ) may be either any of a range of concepts and approaches that relate to statistical methods based on Bayes' theorem Bayes ...'', a superyacht sunk off Palermo in 2024 * * * * * * * * * * * * * * * * * * (BIC) * Widely applicable Bayesian information criterion (WBIC) * * * * * * (BMA) * (BMC) * * * * * * * (BAYOMA) * * * * * * * * * * * * * * * * * * * * * * (PBE) * * * * * See also * * * *, a cryptanalytic process * * * * (BUGS) * * (BATMAN) * * * *, a general ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Record Linkage
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Record linkage is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference. A data set that has undergone RL-oriented reconciliation may be referred to as being ''cross-linked''. Naming conventions "Record linkage" is the term used by statisticians, epidemiologists, and historians, among others, to describe the process of joining records from one data source with another that describe the same entity. However, many other terms are used for this process. Unfortunately, this profusion of terminology has led to few cross- ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |