What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
The scientist’s investigation covers issues in Support vector machine, Machine learning, Artificial intelligence, Activity recognition and Algorithm.
Davide Anguita has included themes like Artificial neural network, Computer hardware and Mathematical optimization in his Support vector machine study.
His Machine learning study combines topics in areas such as Classifier and Quadratic programming.
His Artificial intelligence research includes elements of Preventive maintenance and Reliability.
His Activity recognition study which covers Inertial measurement unit that intersects with Motion, Ambient intelligence, Multiclass classification and Adaptation.
His studies deal with areas such as Kernel method, Kernel and Kernel as well as Algorithm.
His most cited work include:
- A public domain dataset for human activity recognition using smartphones (768 citations)
- Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine (497 citations)
- Transition-Aware Human Activity Recognition Using Smartphones (296 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Support vector machine, Machine learning, Algorithm and Model selection are his primary areas of study.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Rademacher complexity and Pattern recognition.
Davide Anguita combines topics linked to Artificial neural network with his work on Support vector machine.
His work carried out in the field of Machine learning brings together such families of science as Theoretical computer science and Data mining.
His Relevance vector machine study integrates concerns from other disciplines, such as Online machine learning and Active learning.
He combines subjects such as Margin classifier and Sequential minimal optimization with his study of Structured support vector machine.
He most often published in these fields:
- Artificial intelligence (49.21%)
- Support vector machine (36.51%)
- Machine learning (36.51%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (49.21%)
- Machine learning (36.51%)
- Data mining (10.58%)
In recent papers he was focusing on the following fields of study:
Davide Anguita mainly focuses on Artificial intelligence, Machine learning, Data mining, Big data and Rademacher complexity.
His research in Support vector machine and Hyperparameter are components of Artificial intelligence.
His biological study spans a wide range of topics, including Transposition and Mathematical optimization.
His work in Machine learning tackles topics such as Data modeling which are related to areas like Field.
His Data analysis study in the realm of Data mining interacts with subjects such as Key.
His Big data course of study focuses on Extreme learning machine and Sentiment analysis.
Between 2014 and 2021, his most popular works were:
- Transition-Aware Human Activity Recognition Using Smartphones (296 citations)
- Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf (133 citations)
- Machine learning approaches for improving condition-based maintenance of naval propulsion plants (72 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Machine learning
His primary areas of study are Machine learning, Artificial intelligence, Condition-based maintenance, Big data and Support vector machine.
His research integrates issues of Randomized algorithm, Systems architecture, Type generalization and Rademacher complexity in his study of Machine learning.
His work deals with themes such as Algorithm and Differential privacy, which intersect with Artificial intelligence.
His Condition-based maintenance research is multidisciplinary, incorporating elements of Preventive maintenance, Transport engineering, Propulsion, Condition monitoring and Bearing.
His Big data research incorporates elements of Data modeling, Field and Information system.
His Support vector machine research is multidisciplinary, incorporating perspectives in Wearable computer, Probabilistic logic, Activity recognition and Mathematical optimization.
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