Quick extreme learning machine for large-scale classification

Albtoush, Audi; Fernández-Delgado, Manuel; Cernadas, E.; Barro, Senén

doi:10.1007/s00521-021-06727-8

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…An example of this kind is the case with "Time Distributed" NNs, which is a very old concept, dating back to the pioneering work of Waibel and Hinton [55]. A similar discussion is that of Extreme Learning Machines (ELMs), which are new terms for old concepts working on the pioneering use of Moore-Penrose Pseudoinverse, known for their single-lap training process, and which have been proposed to substitute some of the training steps of intermediate convolutional layers [56,57].…”

Section: Study Assessment Premisesmentioning

confidence: 98%

“…appear to be more suitable choices for clinical explanatory diagnostic-help applications), the development of hybrid agent-based applications could benefit from the best of both approaches. Using multilayer extreme learning machines, known for their single-lap training process, could substitute some of the training steps of intermediate convolutional layers [56,57]. Presentation of results on clinically-oriented dashboards, helped by decision-making assistance on random forests, seems to be quite an acceptable approach to clinical explainability (see the work of Wu et al [81] as an example).…”

Section: Final Remarksmentioning

confidence: 99%

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The Role of Data Analytics in the Assessment of Pathological Speech—A Critical Appraisal

et al. 2022

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Pathological voice characterization has received increasing attention over the last 20 years. Hundreds of studies have been published showing inventive approaches with very promising findings. Nevertheless, methodological issues might hamper performance assessment trustworthiness. This study reviews some critical aspects regarding data collection and processing, machine learning-oriented methods, and grounding analytical approaches, with a view to embedding developed clinical decision support tools into the diagnosis decision-making process. A set of 26 relevant studies published since 2010 was selected through critical selection criteria and evaluated. The model-driven (MD) or data-driven (DD) character of the selected approaches is deeply examined considering novelty, originality, statistical robustness, trustworthiness, and clinical relevance. It has been found that before 2020 most of the works examined were more aligned with MD approaches, whereas over the last two years a balanced proportion of DD and MD-based studies was found. A total of 15 studies presented MD characters, whereas seven were mainly DD-oriented, and four shared both profiles. Fifteen studies showed exploratory or prospective advanced statistical analysis. Eighteen included some statistical validation to avail claims. Twenty-two reported original work, whereas the remaining four were systematic reviews of others’ work. Clinical relevance and acceptability by voice specialists were found in 14 out of the 26 works commented on. Methodological issues such as detection and classification performance, training and generalization capability, explainability, preservation of semantic load, clinical acceptance, robustness, and development expenses have been identified as major issues in applying machine learning to clinical support systems. Other important aspects to be taken into consideration are trustworthiness, gender-balance issues, and statistical relevance.

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Section: Study Assessment Premisesmentioning

confidence: 98%