Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Tomaž Stepišnik"'
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-20 (2022)
Abstract Motivation Compound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Com
Externí odkaz:
https://doaj.org/article/4c2bc63a524448e4a5ebd0cf9a1cbec4
Autor:
Matej Petković, Luke Lucas, Jurica Levatić, Martin Breskvar, Tomaž Stepišnik, Ana Kostovska, Panče Panov, Aljaž Osojnik, Redouane Boumghar, José A. Martínez-Heras, James Godfrey, Alessandro Donati, Sašo Džeroski, Nikola Simidjievski, Bernard Ženko, Dragi Kocev
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-8 (2022)
Measurement(s) electric current Technology Type(s) current readings in spacecraft housekeeping telemetry Sample Characteristic - Environment outer space
Externí odkaz:
https://doaj.org/article/f24eda5597eb4c9f93340b8f0ca19f70
Autor:
Nadja Anneliese Ruth Ring, Maria Concetta Volpe, Tomaž Stepišnik, Maria Grazia Mamolo, Panče Panov, Dragi Kocev, Simone Vodret, Sara Fortuna, Antonella Calabretti, Michael Rehman, Andrea Colliva, Pietro Marchesan, Luca Camparini, Thomas Marcuzzo, Rossana Bussani, Sara Scarabellotto, Marco Confalonieri, Tho X. Pham, Giovanni Ligresti, Nunzia Caporarello, Francesco S. Loffredo, Daniele Zampieri, Sašo Džeroski, Serena Zacchigna
Publikováno v:
Cell Death and Disease, Vol 13, Iss 1, Pp 1-12 (2021)
Summary Therapies halting the progression of fibrosis are ineffective and limited. Activated myofibroblasts are emerging as important targets in the progression of fibrotic diseases. Previously, we performed a high-throughput screen on lung fibroblas
Externí odkaz:
https://doaj.org/article/52dc0cfc7d72406b8dc456fc310de27a
Autor:
Tomaž Stepišnik, Dragi Kocev
Publikováno v:
PeerJ Computer Science, Vol 7, p e506 (2021)
Semi-supervised learning combines supervised and unsupervised learning approaches to learn predictive models from both labeled and unlabeled data. It is most appropriate for problems where labeled examples are difficult to obtain but unlabeled exampl
Externí odkaz:
https://doaj.org/article/48ac210817f1400f9562d7232f7d10e2
Autor:
Tomaž Stepišnik, Timothy Finn, Nikola Simidjievski, Richard Southworth, Guillaume Belanger, José Antonio Martínez Heras, Matej Petković, Panče Panov, Sašo Džeroski, Alessandro Donati, Dragi Kocev
Publikováno v:
Advances in Space Research. 69:3909-3920
Publikováno v:
Machine Learning. 109:2213-2241
We address the task of learning ensembles of predictive models for structured output prediction (SOP). We focus on three SOP tasks: multi-target regression (MTR), multi-label classification (MLC) and hierarchical multi-label classification (HMC). In
Publikováno v:
Acta Polytechnica Hungarica. 17:109-128
Autor:
Dragi Kocev, Tomaž Stepišnik
Publikováno v:
PeerJ Computer Science, Vol 7, p e506 (2021)
PeerJ Computer Science
PeerJ Computer Science
Semi-supervised learning combines supervised and unsupervised learning approaches to learn predictive models from both labeled and unlabeled data. It is most appropriate for problems where labeled examples are difficult to obtain but unlabeled exampl
Publikováno v:
Computers in biology and medicine. 130
Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples r
Autor:
Dragi Kocev, Tomaž Stepišnik
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030594909
ISMIS
ISMIS
Decision trees are well established machine learning models that combined in ensembles produce state-of-the-art predictive performance. Predictive clustering trees are a generalization of standard classification and regression trees towards structure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32473dc614731d36ff402a28c38380c4
https://doi.org/10.1007/978-3-030-59491-6_31
https://doi.org/10.1007/978-3-030-59491-6_31