What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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.
Her most cited work include:
- Feature Selection for Unsupervised Learning (720 citations)
- Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors (453 citations)
- Active Learning from Crowds (236 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Artificial intelligence (59.59%)
- Pattern recognition (26.12%)
- Machine learning (23.27%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (59.59%)
- Pattern recognition (26.12%)
- Machine learning (23.27%)
In recent papers she was focusing on the following fields of study:
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
- Kernel which connect with Eigenvalues and eigenvectors and Dimensionality reduction,
- Spectral method which connect with Spectral clustering.
Between 2018 and 2021, her most popular works were:
- Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. (34 citations)
- COPDGene® 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease. (31 citations)
- Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning. (22 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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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