FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W579920800> ?p ?o ?g. }

• W579920800 endingPage "164" @default.
• W579920800 startingPage "153" @default.
• W579920800 abstract "This paper presents an extension of the well-known Extreme Learning Machines (ELMs). The main goal is to provide probabilities as outputs for Multiclass Classification problems. Such information is more useful in practice than traditional crisp classification outputs. In summary, Gaussian Mixture Models are used as post-processing of ELMs. In that context, the proposed global methodology is keeping the advantages of ELMs (low computational time and state of the art performances) and the ability of Gaussian Mixture Models to deal with probabilities. The methodology is tested on 3 toy examples and 3 real datasets. As a result, the global performances of ELMs are slightly improved and the probability outputs are seen to be accurate and useful in practice." @default.
• W579920800 created "2016-06-24" @default.
• W579920800 creator A5016015556 @default.
• W579920800 creator A5035387660 @default.
• W579920800 creator A5040774367 @default.
• W579920800 creator A5050678141 @default.
• W579920800 creator A5056951095 @default.
• W579920800 creator A5072031992 @default.
• W579920800 creator A5072292967 @default.
• W579920800 creator A5078854826 @default.
• W579920800 creator A5083632408 @default.
• W579920800 date "2015-01-01" @default.
• W579920800 modified "2023-09-24" @default.
• W579920800 title "Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models" @default.
• W579920800 cites W1521989125 @default.
• W579920800 cites W1851403712 @default.
• W579920800 cites W1966948031 @default.
• W579920800 cites W1967989262 @default.
• W579920800 cites W2019718627 @default.
• W579920800 cites W2035026907 @default.
• W579920800 cites W2038418568 @default.
• W579920800 cites W2040647667 @default.
• W579920800 cites W2045427344 @default.
• W579920800 cites W2050297026 @default.
• W579920800 cites W2059586463 @default.
• W579920800 cites W2076417350 @default.
• W579920800 cites W2081295504 @default.
• W579920800 cites W2095635024 @default.
• W579920800 cites W2111072639 @default.
• W579920800 cites W2130378394 @default.
• W579920800 cites W2139625147 @default.
• W579920800 cites W2141695047 @default.
• W579920800 cites W2158286838 @default.
• W579920800 cites W2168175751 @default.
• W579920800 cites W2235806098 @default.
• W579920800 cites W2488678869 @default.
• W579920800 cites W2994602700 @default.
• W579920800 doi "https://doi.org/10.1007/978-3-319-19222-2_13" @default.
• W579920800 hasPublicationYear "2015" @default.
• W579920800 type Work @default.
• W579920800 sameAs 579920800 @default.
• W579920800 citedByCount "10" @default.
• W579920800 countsByYear W5799208002016 @default.
• W579920800 countsByYear W5799208002017 @default.
• W579920800 countsByYear W5799208002018 @default.
• W579920800 countsByYear W5799208002019 @default.
• W579920800 countsByYear W5799208002020 @default.
• W579920800 countsByYear W5799208002022 @default.
• W579920800 crossrefType "book-chapter" @default.
• W579920800 hasAuthorship W579920800A5016015556 @default.
• W579920800 hasAuthorship W579920800A5035387660 @default.
• W579920800 hasAuthorship W579920800A5040774367 @default.
• W579920800 hasAuthorship W579920800A5050678141 @default.
• W579920800 hasAuthorship W579920800A5056951095 @default.
• W579920800 hasAuthorship W579920800A5072031992 @default.
• W579920800 hasAuthorship W579920800A5072292967 @default.
• W579920800 hasAuthorship W579920800A5078854826 @default.
• W579920800 hasAuthorship W579920800A5083632408 @default.
• W579920800 hasConcept C119857082 @default.
• W579920800 hasConcept C121332964 @default.
• W579920800 hasConcept C12267149 @default.
• W579920800 hasConcept C123860398 @default.
• W579920800 hasConcept C147789679 @default.
• W579920800 hasConcept C153180895 @default.
• W579920800 hasConcept C154945302 @default.
• W579920800 hasConcept C163716315 @default.
• W579920800 hasConcept C185592680 @default.
• W579920800 hasConcept C2780150128 @default.
• W579920800 hasConcept C41008148 @default.
• W579920800 hasConcept C50644808 @default.
• W579920800 hasConcept C60044698 @default.
• W579920800 hasConcept C61326573 @default.
• W579920800 hasConcept C62520636 @default.
• W579920800 hasConceptScore W579920800C119857082 @default.
• W579920800 hasConceptScore W579920800C121332964 @default.
• W579920800 hasConceptScore W579920800C12267149 @default.
• W579920800 hasConceptScore W579920800C123860398 @default.
• W579920800 hasConceptScore W579920800C147789679 @default.
• W579920800 hasConceptScore W579920800C153180895 @default.
• W579920800 hasConceptScore W579920800C154945302 @default.
• W579920800 hasConceptScore W579920800C163716315 @default.
• W579920800 hasConceptScore W579920800C185592680 @default.
• W579920800 hasConceptScore W579920800C2780150128 @default.
• W579920800 hasConceptScore W579920800C41008148 @default.
• W579920800 hasConceptScore W579920800C50644808 @default.
• W579920800 hasConceptScore W579920800C60044698 @default.
• W579920800 hasConceptScore W579920800C61326573 @default.
• W579920800 hasConceptScore W579920800C62520636 @default.
• W579920800 hasLocation W5799208001 @default.
• W579920800 hasOpenAccess W579920800 @default.
• W579920800 hasPrimaryLocation W5799208001 @default.
• W579920800 hasRelatedWork W2041399278 @default.
• W579920800 hasRelatedWork W2099369243 @default.
• W579920800 hasRelatedWork W2902466377 @default.
• W579920800 hasRelatedWork W2969890106 @default.
• W579920800 hasRelatedWork W3134233996 @default.
• W579920800 hasRelatedWork W3194539120 @default.
• W579920800 hasRelatedWork W4205958290 @default.

• next