Her primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Cluster analysis and Feature selection. Her Artificial intelligence study frequently links to other fields, such as Computer vision. Her work focuses on many connections between Pattern recognition and other disciplines, such as Algorithm, that overlap with her field of interest in Decision tree model.
Jennifer G. Dy regularly ties together related areas like Set in her Machine learning studies. The Cluster analysis study combines topics in areas such as Covariance and Data mining. She interconnects Unsupervised learning, Feature vector, Curse of dimensionality and Dimensionality reduction in the investigation of issues within Feature selection.
Jennifer G. Dy focuses on Artificial intelligence, Pattern recognition, Machine learning, Cluster analysis and Data mining. Her work on Computer vision expands to the thematically related Artificial intelligence. Her Pattern recognition research integrates issues from Artificial neural network and Feature.
Her Machine learning research includes themes of Classifier and Probabilistic logic. Her Cluster analysis research is multidisciplinary, incorporating elements of Feature vector and Dimensionality reduction. Jennifer G. Dy combines subjects such as Algorithm and Image retrieval with her study of Feature vector.
Her main research concerns Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Cluster analysis. Jennifer G. Dy incorporates Artificial intelligence and Transmitter in her studies. Pattern recognition is often connected to Feature in her work.
Her research investigates the connection between Machine learning and topics such as Benchmark that intersect with problems in Pruning and Forgetting. The study incorporates disciplines such as Classifier and Kernel in addition to Deep learning. Her study on Cluster analysis also encompasses disciplines like
Artificial intelligence, Deep learning, Retinopathy of prematurity, Disease and Pattern recognition are her primary areas of study. Her Artificial intelligence research includes elements of Test, Machine learning and Natural language processing. Her Machine learning study combines topics in areas such as Rehabilitation interventions, Wearable computer and Key.
Her research investigates the connection with Deep learning and areas like Convolutional neural network which intersect with concerns in Wireless, Communication channel, Active learning and Image segmentation. Her Disease research incorporates elements of COPD and Spirometry. Her Pattern recognition research is multidisciplinary, incorporating perspectives in Fisher information and Data set.
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Feature Selection for Unsupervised Learning
Jennifer G. Dy;Carla E. Brodley.
Journal of Machine Learning Research (2004)
Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
S. Patel;K. Lorincz;R. Hughes;N. Huggins.
international conference of the ieee engineering in medicine and biology society (2009)
Impact of imputation of missing values on classification error for discrete data
Alireza Farhangfar;Lukasz Kurgan;Jennifer Dy.
Pattern Recognition (2008)
Active Learning from Crowds
Yan Yan;Glenn M. Fung;R mer Rosales;Jennifer G. Dy.
international conference on machine learning (2011)
Feature Subset Selection and Order Identification for Unsupervised Learning
Jennifer G. Dy;Carla E. Brodley.
international conference on machine learning (2000)
Unsupervised feature selection applied to content-based retrieval of lung images
J.G. Dy;C.E. Brodley;A. Kak;L.S. Broderick.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)
Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
James M. Brown;J. Peter Campbell;Andrew Beers;Ken Chang.
JAMA Ophthalmology (2018)
Evolving feature selection
H. Liu;E.R. Dougherty;J.G. Dy;K. Torkkola.
IEEE Intelligent Systems (2005)
Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories.
Erika H. Siegel;Molly K. Sands;Wim Van den Noortgate;Paul Condon.
Psychological Bulletin (2018)
Modeling annotator expertise: Learning when everybody knows a bit of something
Yan Yan;Rómer Rosales;Glenn Fung;Mark W. Schmidt.
international conference on artificial intelligence and statistics (2010)
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