What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary areas of study are Data mining, Artificial intelligence, Machine learning, Theoretical computer science and Smart environment.
The Data mining study combines topics in areas such as Domain, Minimum description length, Graph based and Component.
His studies in Domain integrate themes in fields like Variety and Molecule mining.
His research integrates issues of Concept learning and Remotely operated underwater vehicle in his study of Artificial intelligence.
His study on Active learning and Deep learning is often connected to Epigenomics and Epigenome as part of broader study in Machine learning.
Lawrence B. Holder studied Theoretical computer science and Graph database that intersect with Graph drawing, Kernel method, Link analysis and Decision tree.
His most cited work include:
- Substructure discovery using minimum description length and background knowledge (431 citations)
- Mining Graph Data (407 citations)
- Graph-based data mining (378 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Data mining, Graph, Machine learning and Theoretical computer science are his primary areas of study.
His Pattern recognition research extends to the thematically linked field of Artificial intelligence.
His work in the fields of Data mining, such as Anomaly detection, intersects with other areas such as Substructure.
In his research on the topic of Graph, Data science, Scalability, External Data Representation, Knowledge acquisition and Data compression is strongly related with Knowledge extraction.
Lawrence B. Holder interconnects Graph property, Null graph, Voltage graph and Graph database, Graph in the investigation of issues within Theoretical computer science.
Lawrence B. Holder works mostly in the field of Graph based, limiting it down to topics relating to Concept learning and, in certain cases, Conceptual graph.
He most often published in these fields:
- Artificial intelligence (37.50%)
- Data mining (34.48%)
- Graph (30.17%)
What were the highlights of his more recent work (between 2013-2020)?
- Artificial intelligence (37.50%)
- Graph (30.17%)
- Machine learning (22.84%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Graph, Machine learning, Graph and Data mining.
His work on Deep learning, Activity recognition and Support vector machine as part of general Artificial intelligence study is frequently linked to Smart phone, bridging the gap between disciplines.
The concepts of his Graph study are interwoven with issues in Anomaly detection, Theoretical computer science, Global Positioning System and Data compression.
Lawrence B. Holder combines subjects such as Representation and Epigenetics with his study of Machine learning.
His work in Graph addresses subjects such as Scalability, which are connected to disciplines such as Computer engineering and Local search.
In his research, Lawrence B. Holder undertakes multidisciplinary study on Data mining and Motif.
Between 2013 and 2020, his most popular works were:
- Machine learning for epigenetics and future medical applications (42 citations)
- A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs (38 citations)
- Genomic Clustering of differential DNA methylated regions (epimutations) associated with the epigenetic transgenerational inheritance of disease and phenotypic variation (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Artificial intelligence, Epigenetics, Machine learning, Graph and Genomics.
Artificial intelligence and Pattern recognition are frequently intertwined in his study.
His Machine learning research incorporates elements of Probabilistic logic and Conditional random field.
His work deals with themes such as Butterfly graph, Theoretical computer science, Forbidden graph characterization, Graph and Graph labeling, which intersect with Graph.
His Graph research is multidisciplinary, incorporating perspectives in Entropy, Scalability, Discriminative model and Graph kernel.
His Support vector machine research includes themes of Margin, Parallel algorithm, Data mining and Curse of dimensionality.
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