What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Data mining, Fractional Brownian motion, Wireless sensor network and Machine learning.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Function, Computer vision and Pattern recognition.
His Data mining research is multidisciplinary, incorporating elements of Crowdsourcing, Social media, Analytic hierarchy process and Contrast.
His study in Fractional Brownian motion is interdisciplinary in nature, drawing from both Stochastic process, Estimation theory, Fractal and Fourier transform.
The study incorporates disciplines such as Snapshot, Brooks–Iyengar algorithm, Global network, Dissemination and Data science in addition to Wireless sensor network.
His Machine learning study deals with Correctness intersecting with Heuristic, Social network, Set, Heuristics and Expectation–maximization algorithm.
His most cited work include:
- On truth discovery in social sensing: a maximum likelihood estimation approach (308 citations)
- Extended fractal analysis for texture classification and segmentation (219 citations)
- Global node selection for localization in a distributed sensor network (199 citations)
What are the main themes of his work throughout his whole career to date?
Lance Kaplan mostly deals with Artificial intelligence, Machine learning, Wireless sensor network, Computer vision and Data mining.
His study looks at the intersection of Artificial intelligence and topics like Pattern recognition with Fractal.
His studies deal with areas such as Training set, Inference and Set as well as Machine learning.
In his work, Expectation–maximization algorithm is strongly intertwined with Mathematical optimization, which is a subfield of Wireless sensor network.
When carried out as part of a general Computer vision research project, his work on Automatic target recognition, Image processing, Image quality and Wavelet is frequently linked to work in Proximity sensor, therefore connecting diverse disciplines of study.
His research in Data mining focuses on subjects like Social network, which are connected to Social media.
He most often published in these fields:
- Artificial intelligence (40.46%)
- Machine learning (16.03%)
- Wireless sensor network (15.65%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (40.46%)
- Machine learning (16.03%)
- Bayesian probability (6.87%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Bayesian probability, Artificial neural network and Training set.
Lance Kaplan connects Artificial intelligence with Complex event processing in his study.
Lance Kaplan has researched Machine learning in several fields, including Black box and Reliability.
His biological study spans a wide range of topics, including Calibration, Function and Class.
In his study, Complex system, Representation, Dynamic network analysis and Algorithm is strongly linked to Anomaly detection, which falls under the umbrella field of Artificial neural network.
His Training set research is multidisciplinary, incorporating perspectives in Situational ethics, Probabilistic logic, Markov chain, Bayesian network and Robustness.
Between 2018 and 2021, his most popular works were:
- The Age of Social Sensing (80 citations)
- Spherical Text Embedding (38 citations)
- Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI. (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Lance Kaplan mainly focuses on Artificial intelligence, Bayesian probability, Machine learning, Social learning and Theoretical computer science.
He combines topics linked to Certainty with his work on Artificial intelligence.
His Bayesian probability research incorporates themes from Artificial neural network and Anomaly detection.
Many of his research projects under Machine learning are closely connected to Complex event processing with Complex event processing, tying the diverse disciplines of science together.
His Theoretical computer science research incorporates elements of Paragraph, Euclidean space, Embedding, Word and Sequence.
His Embedding study combines topics in areas such as Feature engineering, Structure, Feature learning, Pairwise comparison and Node.
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