Faster access than browser!
28 relations: Artificial intelligence, Association rule learning, Bayesian network, Ben Taskar, Binary relation, Cluster analysis, Collaborative filtering, Domain model, First-order logic, Formal concept analysis, Fuzzy logic, Grammar induction, Graphical model, Inductive logic programming, Knowledge representation and reasoning, Lecture Notes in Computer Science, Lise Getoor, Machine learning, Markov logic network, Markov random field, Probabilistic soft logic, Reason, Record linkage, Social network, Statistical classification, Statistical inference, Uncertainty, Universal quantification.
Artificial intelligence
Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.
New!!: Statistical relational learning and Artificial intelligence · See more »
Association rule learning
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.
New!!: Statistical relational learning and Association rule learning · See more »
Bayesian network
A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
New!!: Statistical relational learning and Bayesian network · See more »
Ben Taskar
Ben Taskar (March 3, 1977 – November 18, 2013) was a professor and researcher in the area of machine learning and applications to computational linguistics and computer vision.
New!!: Statistical relational learning and Ben Taskar · See more »
Binary relation
In mathematics, a binary relation on a set A is a set of ordered pairs of elements of A. In other words, it is a subset of the Cartesian product A2.
New!!: Statistical relational learning and Binary relation · See more »
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).
New!!: Statistical relational learning and Cluster analysis · See more »
Collaborative filtering
Collaborative filtering (CF) is a technique used by recommender systems.
New!!: Statistical relational learning and Collaborative filtering · See more »
Domain model
In software engineering, a domain model is a conceptual model of the domain that incorporates both behavior and data.
New!!: Statistical relational learning and Domain model · See more »
First-order logic
First-order logic—also known as first-order predicate calculus and predicate logic—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science.
New!!: Statistical relational learning and First-order logic · See more »
Formal concept analysis
Formal concept analysis (FCA) is a principled way of deriving a concept hierarchy or formal ontology from a collection of objects and their properties.
New!!: Statistical relational learning and Formal concept analysis · See more »
Fuzzy logic
Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1.
New!!: Statistical relational learning and Fuzzy logic · See more »
Grammar induction
Grammar induction (or grammatical inference) is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects.
New!!: Statistical relational learning and Grammar induction · See more »
Graphical model
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables.
New!!: Statistical relational learning and Graphical model · See more »
Inductive logic programming
Inductive logic programming (ILP) is a subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge and hypotheses.
New!!: Statistical relational learning and Inductive logic programming · See more »
Knowledge representation and reasoning
Knowledge representation and reasoning (KR, KR², KR&R) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.
New!!: Statistical relational learning and Knowledge representation and reasoning · See more »
Lecture Notes in Computer Science
Springer Lecture Notes in Computer Science (LNCS) is a series of computer science books published by Springer Science+Business Media (formerly Springer-Verlag) since 1973.
New!!: Statistical relational learning and Lecture Notes in Computer Science · See more »
Lise Getoor
Lise Getoor is a professor in the Computer Science Department, at the University of California, Santa Cruz, and an adjunct professor in the Computer Science Department at the University of Maryland, College Park.
New!!: Statistical relational learning and Lise Getoor · See more »
Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
New!!: Statistical relational learning and Machine learning · See more »
Markov logic network
A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference.
New!!: Statistical relational learning and Markov logic network · See more »
Markov random field
In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph.
New!!: Statistical relational learning and Markov random field · See more »
Probabilistic soft logic
Probabilistic soft logic (PSL) is a SRL framework for collective, probabilistic reasoning in relational domains.
New!!: Statistical relational learning and Probabilistic soft logic · See more »
Reason
Reason is the capacity for consciously making sense of things, establishing and verifying facts, applying logic, and changing or justifying practices, institutions, and beliefs based on new or existing information.
New!!: Statistical relational learning and Reason · See more »
Record linkage
Record linkage (RL) 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).
New!!: Statistical relational learning and Record linkage · See more »
Social network
A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors.
New!!: Statistical relational learning and Social network · See more »
Statistical classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
New!!: Statistical relational learning and Statistical classification · See more »
Statistical inference
Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution.
New!!: Statistical relational learning and Statistical inference · See more »
Uncertainty
Uncertainty has been called "an unintelligible expression without a straightforward description".
New!!: Statistical relational learning and Uncertainty · See more »
Universal quantification
In predicate logic, a universal quantification is a type of quantifier, a logical constant which is interpreted as "given any" or "for all".
New!!: Statistical relational learning and Universal quantification · See more »
Redirects here:
Probabilistic relational model, Relational probabilistic model.
References
[1] https://en.wikipedia.org/wiki/Statistical_relational_learning
