Her primary areas of investigation include Artificial intelligence, Bayesian network, Machine learning, Knowledge representation and reasoning and Probabilistic logic. Her Artificial intelligence research incorporates themes from Probability distribution, Theoretical computer science and Process. Her Theoretical computer science study integrates concerns from other disciplines, such as Generalization, Partition and Completeness.
Her research integrates issues of Evolutionary algorithm, Software engineering, Missing data and Search algorithm in her study of Bayesian network. Her Machine learning research incorporates elements of Set and Data mining. Her study looks at the intersection of Probabilistic logic and topics like Data science with Ontology, Human intelligence, Syntax, Semantic interoperability and Knowledge sharing.
Kathryn B. Laskey mainly focuses on Artificial intelligence, Bayesian network, Machine learning, Probabilistic logic and Ontology. Her research investigates the connection between Artificial intelligence and topics such as Process that intersect with issues in Knowledge base. Her Bayesian network research incorporates themes from Probability distribution, Theoretical computer science, Random variable, Representation and Knowledge representation and reasoning.
Kathryn B. Laskey has included themes like Variable-order Bayesian network and Data mining in her Machine learning study. She focuses mostly in the field of Probabilistic logic, narrowing it down to matters related to Data science and, in some cases, Interoperability and Decision support system. Her biological study deals with issues like OWL-S, which deal with fields such as Semantic Web Rule Language.
Kathryn B. Laskey mainly focuses on Artificial intelligence, Bayesian network, Inference, Machine learning and Ontology. Kathryn B. Laskey combines subjects such as Pattern recognition, Process and Natural language processing with her study of Artificial intelligence. Her study in Bayesian network is interdisciplinary in nature, drawing from both Random variable, Knowledge representation and reasoning, Relational database, Use case and Sensor fusion.
Her Inference research integrates issues from Time complexity, Algorithm, Reduction, Insider threat and Data science. Her research in Machine learning intersects with topics in Probability distribution and Test data generation. Her Ontology study incorporates themes from Ontology, Key, Abstraction and Geolocation.
Kathryn B. Laskey focuses on Artificial intelligence, Ontology, Data mining, Machine learning and Bayesian network. Artificial intelligence is closely attributed to Pattern recognition in her research. Her Ontology study combines topics from a wide range of disciplines, such as Ontology, Sociotechnical system, Knowledge management and Natural language processing.
Kathryn B. Laskey has researched Data mining in several fields, including Data modeling, Uncertainty analysis and Rotation formalisms in three dimensions. As part of the same scientific family, Kathryn B. Laskey usually focuses on Machine learning, concentrating on Process and intersecting with Ontology-based data integration and Process ontology. Representation, Set, Conditional dependence and Use case is closely connected to Relational database in her research, which is encompassed under the umbrella topic of Bayesian network.
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Stochastic blockmodels: First steps
Paul W. Holland;Kathryn Blackmond Laskey;Samuel Leinhardt.
Social Networks (1983)
MEBN: A language for first-order Bayesian knowledge bases
Kathryn Blackmond Laskey.
Artificial Intelligence (2008)
Sensitivity analysis for probability assessments in Bayesian networks
uncertainty in artificial intelligence (1995)
Network fragments: representing knowledge for constructing probabilistic models
Kathryn Blackmond Laskey;Suzanne M. Mahoney.
uncertainty in artificial intelligence (1997)
PR-OWL: a Bayesian ontology language for the semantic web
Paulo Cesar G. Da Costa;Kathryn B. Laskey;Kenneth J. Laskey.
international semantic web conference (2005)
PR-OWL: A Framework for Probabilistic Ontologies
Paulo C. G. Costa;Kathryn B. Laskey.
formal ontology in information systems (2006)
Bayesian semantics for the semantic web
Paulo Cesar G. Da Costa;Kathryn Blackmond Laskey.
Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies
Laura Martignon;Gustavo Deco;Kathryn Laskey;Mathew Diamond.
Neural Computation (2000)
Towards unbiased evaluation of uncertainty reasoning: The URREF ontology
Paulo C. G. Costa;Kathryn B. Laskey;Erik Blasch;Anne-Laure Jousselme.
international conference on information fusion (2012)
Assumptions, beliefs and probabilities
K. B. Laskey;P. E. Lehner.
Artificial Intelligence (1989)
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