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pro vyhledávání: '"Preez, Johan A. du"'
Underwater acoustic monitoring systems record many hours of audio data for marine research, making fast and reliable non-causal signal detection paramount. Such detectors assist in reducing the amount of labor required for signal annotations, which o
Externí odkaz:
http://arxiv.org/abs/2211.01065
Publikováno v:
Proceedings of the 11'th International Heavy Haul Association Conference (IHHA 2017), pages 799-805 Cape Town, South Africa
Rail breaks are one of the most common causes of derailments internationally. This is no different for the South African Iron Ore line. Many rail breaks occur as a heavy-haul train passes over a crack, large defect or defective weld. In such cases, i
Externí odkaz:
http://arxiv.org/abs/2208.11940
Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in
Externí odkaz:
http://arxiv.org/abs/2208.09299
Latent Dirichlet Allocation (LDA) is a probabilistic model used to uncover latent topics in a corpus of documents. Inference is often performed using variational Bayes (VB) algorithms, which calculate a lower bound to the posterior distribution over
Externí odkaz:
http://arxiv.org/abs/2111.01480
Autor:
Homan, Dewald, Preez, Johan A. du
Prior work on plant species classification predominantly focuses on building models from isolated plant attributes. Hence, there is a need for tools that can assist in species identification in the natural world. We present a novel and robust two-fol
Externí odkaz:
http://arxiv.org/abs/2110.03994
Publikováno v:
In Science and Information Conference (pp. 723-746). Springer, Cham (2022)
Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in
Externí odkaz:
http://arxiv.org/abs/2110.00635
Novel categories are commonly defined as those unobserved during training but present during testing. However, partially labelled training datasets can contain unlabelled training samples that belong to novel categories, meaning these can be present
Externí odkaz:
http://arxiv.org/abs/2002.01368
Akademický článek
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Unified machine learning: Classification with simultaneous observed and unobserved novelty detection
A unified approach of Positive and Unlabelled (PU)-learning, Semi-Supervised Learning (SSL), and Open-Set Recognition (OSR) would significantly enhance the development of cost-efficient application-grade classifiers. However, previous attempts have c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2c71cf11534a05a7dc5c4ff518c5139
http://arxiv.org/abs/2002.01368
http://arxiv.org/abs/2002.01368