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pro vyhledávání: '"68T09, 68T10"'
This paper introduces to a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks, specifically addressing a vowel phonemes classification case study within the Automatic Speech Recogn
Externí odkaz:
http://arxiv.org/abs/2410.05320
The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detection algorithm for a specific task, such as disease classification. Eight key algo
Externí odkaz:
http://arxiv.org/abs/2404.12408
Publikováno v:
Artificial Neural Networks and Machine Learning ICANN 2023. Lecture Notes in Computer Science, vol 14254, pp 516-529. Springer, Cham
High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers. However, this is more of a rule of thumb t
Externí odkaz:
http://arxiv.org/abs/2311.07579
Autor:
Sadiq, Ismail, Perez-Alday, Erick A., Shah, Amit J., Rad, Ali Bahrami, Sameni, Reza, Clifford, Gari D.
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morphologies and abnormalities specific to a given condition - T-wave Alter
Externí odkaz:
http://arxiv.org/abs/2112.15442
Autor:
Parisi, Luca
This study presents the m-arcsinh Kernel ('m-ar-K') Fast Independent Component Analysis ('FastICA') method ('m-ar-K-FastICA') for feature extraction. The kernel trick has enabled dimensionality reduction techniques to capture a higher extent of non-l
Externí odkaz:
http://arxiv.org/abs/2108.07908
Principal components analysis has been used to reduce the dimensionality of datasets for a long time. In this paper, we will demonstrate that in mode detection the components of smallest variance, the pettiest components, are more important. We prove
Externí odkaz:
http://arxiv.org/abs/2101.04288
Autor:
Benato, Barbara Caroline, Gomes, Jancarlo Ferreira, Telea, Alexandru Cristian, Falcão, Alexandre Xavier
While convolutional neural networks need large labeled sets for training images, expert human supervision of such datasets can be very laborious. Proposed solutions propagate labels from a small set of supervised images to a large set of unsupervised
Externí odkaz:
http://arxiv.org/abs/2008.00558
Autor:
Benato, Barbara Caroline, Gomes, Jancarlo Ferreira, Telea, Alexandru Cristian, Falcão, Alexandre Xavier
Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analys
Externí odkaz:
http://arxiv.org/abs/2007.13689
Principal components analysis has been used to reduce the dimensionality of datasets for a long time. In this paper, we will demonstrate that in mode detection the components of smallest variance, the pettiest components, are more important. We prove
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5514285754c7328e4feb9563e972b8ee
http://arxiv.org/abs/2101.04288
http://arxiv.org/abs/2101.04288
Autor:
Benato, B., Gomes, J., Telea, A., Falcao, A., Sub Visualisation and Graphics, Visualisation and Graphics
Publikováno v:
Pattern Recognition, 109. Elsevier
Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60de7c5c1b4e1e6285e75b8470fa67ca
https://dspace.library.uu.nl/handle/1874/410855
https://dspace.library.uu.nl/handle/1874/410855