Her primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Algorithm and Evolutionary algorithm. Her work on Convolutional neural network and Test set as part of general Artificial intelligence study is frequently linked to Protein engineering, therefore connecting diverse disciplines of science. Her studies deal with areas such as Protein function, Sequence analysis, Small molecule and Peptide sequence as well as Pattern recognition.
Her Supervised learning, Instance-based learning and Instance selection study in the realm of Machine learning connects with subjects such as Significant difference. Her study in the field of Linear programming also crosses realms of Network analysis. Her Evolutionary algorithm research includes themes of Representation, Genetic algorithm, Local search and Heuristic.
Artificial intelligence, Pattern recognition, Theoretical computer science, Machine learning and Algorithm are her primary areas of study. In her work, Cluster analysis is strongly intertwined with Data mining, which is a subfield of Artificial intelligence. Her Pattern recognition study integrates concerns from other disciplines, such as Domain adaptation, Feature and Bioinformatics.
Elena Marchiori focuses mostly in the field of Theoretical computer science, narrowing it down to topics relating to Programming language and, in certain cases, Constraint logic programming. Her research investigates the connection between Algorithm and topics such as Genetic algorithm that intersect with problems in Heuristic. In Convolutional neural network, Elena Marchiori works on issues like Segmentation, which are connected to Magnetic resonance imaging.
Elena Marchiori focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Gaussian process and Deep learning. Her Artificial intelligence research includes elements of Domain adaptation, Domain and Machine learning. Her Pattern recognition research integrates issues from Artificial neural network and Hyperintensity.
Her Convolutional neural network research is multidisciplinary, incorporating elements of Transfer of learning, Test data and Test set. Her work is dedicated to discovering how Algorithm, Convolution are connected with Dependency and other disciplines. The study incorporates disciplines such as Data mining and Cluster analysis in addition to Non-negative matrix factorization.
Her primary scientific interests are in Artificial intelligence, Pattern recognition, Convolutional neural network, Segmentation and Test set. She is interested in Deep learning, which is a branch of Artificial intelligence. She has researched Pattern recognition in several fields, including Test data and Statistics.
Her research on Convolutional neural network concerns the broader Machine learning. Her Segmentation study combines topics from a wide range of disciplines, such as Magnetic resonance imaging and Hyperintensity. Elena Marchiori studied Test set and Artificial neural network that intersect with Image processing.
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Gaussian interaction profile kernels for predicting drug–target interaction
Twan van Laarhoven;Sander B. Nabuurs;Elena Marchiori.
Breakpoint identification and smoothing of array comparative genomic hybridization data
Kees Jong;Elena Marchiori;Gerrit Meijer;A. V. D. Vaart.
Evolutionary Algorithms with On-the-Fly Population Size Adjustment
A. E. Eiben;Elena Marchiori;V. A. Valko.
parallel problem solving from nature (2004)
Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile
Twan van Laarhoven;Elena Marchiori.
PLOS ONE (2013)
Reasoning about Prolog programs: from modes through types to assertions
Krzysztof R. Apt;Elena Marchiori.
Formal Aspects of Computing (1994)
Sample handling for mass spectrometric proteomic investigations of human sera.
Mikkel West-Nielsen;Estrid V. Høgdall;Elena Marchiori;Claus K. Høgdall.
Analytical Chemistry (2005)
Evolutionary algorithms for the satisfiability problem
Jens Gottlieb;Elena Marchiori;Claudio Rossi.
Evolutionary Computation (2002)
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Mohsen Ghafoorian;Mohsen Ghafoorian;Alireza Mehrtash;Alireza Mehrtash;Tina Kapur;Nico Karssemeijer.
medical image computing and computer assisted intervention (2017)
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.
Mohsen Ghafoorian;Nico Karssemeijer;Tom Heskes;Inge W. M. van Uden.
Scientific Reports (2017)
An Evolutionary Algorithm for Large Scale Set Covering Problems with Application to Airline Crew Scheduling
Elena Marchiori;Adri Steenbeek.
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight (2000)
Profile was last updated on December 6th, 2021.
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