Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Benjamin Schelling"'
Autor:
Sarah J. Longo, Ryan St. Pierre, Sarah Bergbreiter, Suzanne Cox, Benjamin Schelling, S. N. Patek
Publikováno v:
The Journal of experimental biology.
The smallest, fastest, repeated-use movements are propelled by power-dense elastic mechanisms, yet the key to their energetic control may be found in the latch-like mechanisms that mediate transformation from elastic potential energy to kinetic energ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::745ea1db28f35fabe9c48e626f26bc8f
https://pubmed.ncbi.nlm.nih.gov/36606724
https://pubmed.ncbi.nlm.nih.gov/36606724
Autor:
Martin Perdacher, Kateřina Hlaváčková-Schindler, Lukas Miklautz, Benjamin Schelling, Robert Fritze, Can Altinigneli, Maximilian Leodolter, Sahar Behzadi, Claudia Plant, Lena Greta Marie Bauer, Ylli Sadikaj
Publikováno v:
Datenbank-Spektrum. 20:71-79
How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ea449bd24368ede25c0f1000d902c64
https://doi.org/10.1007/s13222-020-00337-9
https://doi.org/10.1007/s13222-020-00337-9
Publikováno v:
KDD
The combination of clustering with Deep Learning has gained much attention in recent years. Unsupervised neural networks like autoencoders can autonomously learn the essential structures in a data set. This idea can be combined with clustering object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ffd681e0062f132bccc9e97e953e775
https://doi.org/10.1145/3447548.3467316
https://doi.org/10.1145/3447548.3467316
Autor:
Suzanne J. Kelson, Antonio Vitor Berganton De Souza, Pallab K. Sarker, Takayuki Tsukui, Benjamin Schelling, Anne R. Kapuscinski, Madilyn M. Gamble, Devin S. Fitzgerald
Publikováno v:
Elementa: Science of the Anthropocene. 9
Aquaculture is the fastest growing food production sector and currently supplies almost 50% of fish for human consumption worldwide. There are significant barriers to the continued growth of industrial aquaculture, including high production costs and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28e55e7e1749d6a74b2af70233969a5d
https://doi.org/10.1525/elementa.2020.00170
https://doi.org/10.1525/elementa.2020.00170
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676575
ECML/PKDD (1)
ECML/PKDD (1)
For successful clustering, an algorithm needs to find the boundaries between clusters. While this is comparatively easy if the clusters are compact and non-overlapping and thus the boundaries clearly defined, features where the clusters blend into ea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f8a375e4a53057f4facbf1f91f6ab4a
https://doi.org/10.1007/978-3-030-67658-2_6
https://doi.org/10.1007/978-3-030-67658-2_6
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030590024
DEXA (1)
DEXA (1)
We present here a new parameter-free clustering algorithm that does not impose any assumptions on the data. Based solely on the premise that close data points are more likely to be in the same cluster, it can autonomously create clusters. Neither the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::115e851b7e095a8b458cf6c654789bd9
https://doi.org/10.1007/978-3-030-59003-1_15
https://doi.org/10.1007/978-3-030-59003-1_15
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783030474355
PAKDD (2)
PAKDD (2)
Granger causality for time series states that a cause improves the predictability of its effect. That is, given two time series x and y, we are interested in detecting the causal relations among them considering the previous observations of both time
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5c8513dfe50c914174371843b8a9354
https://doi.org/10.1007/978-3-030-47436-2_56
https://doi.org/10.1007/978-3-030-47436-2_56
Autor:
Claudia Plant, Benjamin Schelling
A data set might have a well-defined structure, but this does not necessarily lead to good clustering results. If the structure is hidden in an unfavourable scaling, clustering will usually fail. The aim of this work is to present techniques—DipSca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89733bdac4ea1bc7b3c75ccad3ef4d34
https://phaidra.univie.ac.at/o:1073126
https://phaidra.univie.ac.at/o:1073126
Autor:
Benjamin Schelling, Claudia Plant
Publikováno v:
2018 IEEE International Conference on Data Mining (ICDM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82d42bb095b0154e81fca62740cf58d9
https://doi.org/10.1109/icdm.2018.00056
https://doi.org/10.1109/icdm.2018.00056
Autor:
Benjamin Schelling, Claudia Plant
Publikováno v:
Big Data Analytics and Knowledge Discovery ISBN: 9783319985381
DaWaK
DaWaK
K-Means is one of the most important data mining techniques for scientists who want to analyze their data. But K-Means has the disadvantage that it is unable to handle noise data points. This paper proposes a technique that can be applied to the k-me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a92fc1f6fb4bb8d49ac61424a8c9c980
https://doi.org/10.1007/978-3-319-98539-8_11
https://doi.org/10.1007/978-3-319-98539-8_11