His primary scientific interests are in Artificial intelligence, Machine learning, Markov decision process, Data mining and Probabilistic logic. His study ties his expertise on Pattern recognition together with the subject of Artificial intelligence. His Machine learning research is multidisciplinary, relying on both Bayesian probability, Heuristics, Heuristic and Hindsight bias.
His Markov decision process research integrates issues from Domain, Control and Mixture model. His research in the fields of Anomaly detection overlaps with other disciplines such as Anomaly. In his study, Language bias and Key is strongly linked to Mathematical optimization, which falls under the umbrella field of Probabilistic logic.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Reinforcement learning, Mathematical optimization and Data mining. Alan Fern focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Heuristics and, in certain cases, Heuristic. His research integrates issues of Beam search and Bayesian probability in his study of Machine learning.
The various areas that Alan Fern examines in his Reinforcement learning study include Robot, Human–computer interaction, Adaptation and Control. In his work, Domain is strongly intertwined with Task, which is a subfield of Human–computer interaction. Alan Fern works mostly in the field of Mathematical optimization, limiting it down to topics relating to Markov decision process and, in certain cases, Theoretical computer science, as a part of the same area of interest.
The scientist’s investigation covers issues in Artificial intelligence, Reinforcement learning, Machine learning, Data mining and Anomaly detection. His work carried out in the field of Artificial intelligence brings together such families of science as Key and Natural language processing. His study in Reinforcement learning is interdisciplinary in nature, drawing from both Robot, Cognitive psychology, Control and Human–computer interaction.
His Machine learning research includes themes of Tree and Algorithm design. His study focuses on the intersection of Data mining and fields such as Process with connections in the field of Wireless sensor network and Convolution. His research in Anomaly detection tackles topics such as Detector which are related to areas like Supervised learning, Identification and Probably approximately correct learning.
His main research concerns Artificial intelligence, Reinforcement learning, Machine learning, Anomaly detection and Data mining. In general Artificial intelligence study, his work on Benchmark, Feature extraction and Recurrent neural network often relates to the realm of Bottleneck and Work, thereby connecting several areas of interest. His Benchmark research is multidisciplinary, incorporating perspectives in Training set, Baseline and Test set.
His Recurrent neural network research includes elements of Range, Interpretability and Representation. The study incorporates disciplines such as Focus and Human–computer interaction in addition to Reinforcement learning. His Anomaly detection study integrates concerns from other disciplines, such as Tree, Ranking and False positive paradox.
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.
FF-Replan: a baseline for probabilistic planning
Sungwook Yoon;Alan Fern;Robert Givan.
international conference on automated planning and scheduling (2007)
Multi-task reinforcement learning: a hierarchical Bayesian approach
Aaron Wilson;Alan Fern;Soumya Ray;Prasad Tadepalli.
international conference on machine learning (2007)
UCT for tactical assault planning in real-time strategy games
Radha-Krishna Balla;Alan Fern.
international joint conference on artificial intelligence (2009)
Discriminatively trained particle filters for complex multi-object tracking
Rob Hess;Alan Fern.
computer vision and pattern recognition (2009)
Probabilistic planning via determinization in hindsight
Sungwook Yoon;Alan Fern;Robert Givan;Subbarao Kambhampati.
national conference on artificial intelligence (2008)
Online Ensemble Learning: An Empirical Study
Alan Fern;Robert Givan.
Machine Learning (2003)
Approximate policy iteration with a policy language bias: solving relational Markov decision processes
Alan Fern;Sungwook Yoon;Robert Givan.
Journal of Artificial Intelligence Research (2006)
Detecting insider threats in a real corporate database of computer usage activity
Ted E. Senator;Henry G. Goldberg;Alex Memory;William T. Young.
knowledge discovery and data mining (2013)
Probabilistic event logic for interval-based event recognition
William Brendel;Alan Fern;Sinisa Todorovic.
computer vision and pattern recognition (2011)
Approximate Policy Iteration with a Policy Language Bias
Alan Fern;Sungwook Yoon;Robert Givan.
neural information processing systems (2003)
Profile was last updated on December 6th, 2021.
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