Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Alexander Tatarchuk"'
Publikováno v:
2021 3rd International Conference on Information Technology and Computer Communications.
Estimation of dependencies from empirical data in a growing class of models is inevitably concerned with choosing the value of a structural parameter responsible for the model’s complexity. The most popular cross-validation schemes, in particular,
Autor:
Alexander Tatarchuk, Vadim Mottl, Ilya Pugach, Valentina Sulimova, Alexey Morozov, Olga Krasotkina
Publikováno v:
2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON).
Usually, when speaking about dependence estimation in big sets of empirical data, it is adopted to suggest that the set of precedents does not fit in the memory of one computer, and some technology of distributed computing is required. However, even
Publikováno v:
ICPR
The relational approach to dependency estimation entails the selection of a sufficiently compact 'relevance' subset of training-set objects with which any newly occurring object may be compared in order to estimate its hidden target characteristics.
Publikováno v:
Pattern Recognition in Bioinformatics ISBN: 9783319091914
PRIB
PRIB
Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c7463bc6881ca96b626d0b3e7002590
https://doi.org/10.1007/978-3-319-09192-1_9
https://doi.org/10.1007/978-3-319-09192-1_9
Publikováno v:
Multiple Classifier Systems ISBN: 9783642215568
MCS
MCS
It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91acff27bded2324f800aa69df80784e
https://doi.org/10.1007/978-3-642-21557-5_15
https://doi.org/10.1007/978-3-642-21557-5_15
Publikováno v:
IEEE Transactions on Information Forensics and Security
In multimodal biometric information fusion, it is common to encounter missing modalities in which matching cannot be performed. As a result, at the match score level, this implies that scores will be missing. We address the multimodal fusion problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8f3ce73cbd4de0d30623167d99734d4
https://surrey.eprints-hosting.org/111075/
https://surrey.eprints-hosting.org/111075/
Publikováno v:
Multiple Classifier Systems ISBN: 9783642121265
MCS
MCS
The Support Kernel Machine (SKM) and the Relevance Kernel Machine (RKM) are two principles for selectively combining object-representation modalities of different kinds by means of incorporating supervised selectivity into the classical kernel-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4676391d3f3640d245cb2788f21eca82
https://doi.org/10.1007/978-3-642-12127-2_17
https://doi.org/10.1007/978-3-642-12127-2_17
Publikováno v:
Multiple Classifier Systems ISBN: 9783642023255
MCS
MCS
We consider the problem of multi-modal pattern recognition under the assumption that a kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95d144c3eac056ac1b1af904926bb305
Publikováno v:
Multiple Classifier Systems ISBN: 9783642023255
MCS
MCS
We have previously introduced, in purely theoretical terms, the notion of neutral point substitution for missing kernel data in multimodal problems. In particular, it was demonstrated that when modalities are maximally disjoint, the method is precise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::979b429cd171ed2cc2350bbc152ee7c0
Publikováno v:
Multiple Classifier Systems ISBN: 9783540724810
MCS
MCS
Multiple modalities present potential difficulties for kernel-based pattern recognition in consequence of the lack of inter-modal kernel measures. This is particularly apparent when training sets for the differing modalities are disjoint. Thus, while
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::934610318e498354ae79c01151fcc923
https://doi.org/10.1007/978-3-540-72523-7_2
https://doi.org/10.1007/978-3-540-72523-7_2