2013 - ACM Fellow For contributions to probabilistic and statistical approaches to data mining and machine learning.
His scientific interests lie mostly in Artificial intelligence, Data mining, Machine learning, Cluster analysis and Context. His research in Artificial intelligence intersects with topics in Algorithm, Markov model and Pattern recognition. His Data mining research includes themes of Data set, Series and Data science.
The concepts of his Data science study are interwoven with issues in Field, Data stream mining and Knowledge extraction. The Machine learning study combines topics in areas such as Inference and Knowledge acquisition. The various areas that Padhraic Smyth examines in his Cluster analysis study include Mixture model, Geopotential height, The Internet and Data visualization.
Padhraic Smyth spends much of his time researching Artificial intelligence, Machine learning, Data mining, Inference and Pattern recognition. Probabilistic logic, Artificial neural network, Hidden Markov model, Topic model and Statistical model are subfields of Artificial intelligence in which his conducts study. His study in Machine learning is interdisciplinary in nature, drawing from both Mixture model, Set and Gibbs sampling.
His Data mining research incorporates themes from Cluster analysis and Data set. His Inference study incorporates themes from Bayesian inference, Bayesian probability, Markov chain Monte Carlo, Latent variable model and Algorithm. His research combines Data science and Knowledge extraction.
The scientist’s investigation covers issues in Artificial intelligence, Inference, Machine learning, Prior probability and Bayesian probability. His Artificial intelligence study integrates concerns from other disciplines, such as Geolocation and Pattern recognition. His Inference research integrates issues from Black box, Algorithm, Latent variable and Bayesian inference.
His Machine learning research is multidisciplinary, relying on both Variety and Gating. His studies in Ranking integrate themes in fields like Mixture model, Recurrent neural network and Data mining. His Data mining study combines topics from a wide range of disciplines, such as Spatial analysis and Association.
His primary areas of investigation include Artificial intelligence, Inference, Data science, Coding and Machine learning. Padhraic Smyth regularly ties together related areas like Psychotherapist in his Artificial intelligence studies. His work deals with themes such as Prior probability, Artificial neural network, Generalized linear model, Algorithm and Pattern recognition, which intersect with Inference.
His biological study spans a wide range of topics, including Bayesian probability, Markov chain Monte Carlo, Markov model, Variable-order Markov model and Variable-order Bayesian network. His Data science research is multidisciplinary, incorporating elements of Student activities, Change detection and Component. His work on Sentiment analysis as part of his general Machine learning study is frequently connected to Selection, thereby bridging the divide between different branches of science.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
From Data Mining to Knowledge Discovery in Databases
Usama M. Fayyad;Gregory Piatetsky-Shapiro;Padhraic Smyth.
Ai Magazine (1996)
Principles of data mining
David J. Hand;Heikki Mannila;Padhraic Smyth.
podm (2001)
The KDD process for extracting useful knowledge from volumes of data
Usama Fayyad;Gregory Piatetsky-Shapiro;Padhraic Smyth.
Communications of The ACM (1996)
From data mining to knowledge discovery: an overview
Usama M. Fayyad;Gregory Piatetsky-Shapiro;Padhraic Smyth.
knowledge discovery and data mining (1996)
Knowledge discovery and data mining: towards a unifying framework
Usama Fayyad;Gregory Piatetsky-Shapiro;Padhraic Smyth.
knowledge discovery and data mining (1996)
The author-topic model for authors and documents
Michal Rosen-Zvi;Thomas Griffiths;Mark Steyvers;Padhraic Smyth.
uncertainty in artificial intelligence (2004)
Principles of Data Mining
David J. Hand;Padhraic Smyth;Heikki Mannila.
(2001)
Rule discovery from time series
Gautam Das;King-Ip Lin;Heikki Mannila;Gopal Renganathan.
knowledge discovery and data mining (1998)
Probabilistic author-topic models for information discovery
Mark Steyvers;Padhraic Smyth;Michal Rosen-Zvi;Thomas Griffiths.
knowledge discovery and data mining (2004)
A Spectral Clustering Approach To Finding Communities in Graph.
Scott White;Padhraic Smyth.
siam international conference on data mining (2005)
Profile was last updated on December 6th, 2021.
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