FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2410116847> ?p ?o ?g. }

• W2410116847 endingPage "1951" @default.
• W2410116847 startingPage "1939" @default.
• W2410116847 abstract "It is well known that the architecture of the extreme learning machine (ELM) significantly affects its performance and how to determine a suitable set of hidden neurons is recognized as a key issue to some extent. The leave-one-out cross-validation (LOO-CV) is usually used to select a model with good generalization performance among potential candidates. The primary reason for using the LOO-CV is that it is unbiased and reliable as long as similar distribution exists in the training and testing data. However, the LOO-CV has rarely been implemented in practice because of its notorious slow execution speed. In this paper, an efficient LOO-CV formula and an efficient LOO-CV-based ELM (ELOO-ELM) algorithm are proposed. The proposed ELOO-ELM algorithm can achieve fast learning speed similar to the original ELM without compromising the reliability feature of the LOO-CV. Furthermore, minimal user intervention is required for the ELOO-ELM, thus it can be easily adopted by nonexperts and implemented in automation processes. Experimentation studies on benchmark datasets demonstrate that the proposed ELOO-ELM algorithm can achieve good generalization with limited user intervention while retaining the efficiency feature." @default.
• W2410116847 created "2016-06-24" @default.
• W2410116847 creator A5035486559 @default.
• W2410116847 creator A5040965135 @default.
• W2410116847 creator A5070629154 @default.
• W2410116847 date "2016-08-01" @default.
• W2410116847 modified "2023-10-17" @default.
• W2410116847 title "An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention" @default.
• W2410116847 cites W1968886642 @default.
• W2410116847 cites W1968918799 @default.
• W2410116847 cites W1972782958 @default.
• W2410116847 cites W1980713635 @default.
• W2410116847 cites W1986278072 @default.
• W2410116847 cites W1987390907 @default.
• W2410116847 cites W1990938413 @default.
• W2410116847 cites W1993717606 @default.
• W2410116847 cites W1995271453 @default.
• W2410116847 cites W1995436378 @default.
• W2410116847 cites W1996127671 @default.
• W2410116847 cites W2002728347 @default.
• W2410116847 cites W2007146523 @default.
• W2410116847 cites W2008543104 @default.
• W2410116847 cites W2009047869 @default.
• W2410116847 cites W2016355702 @default.
• W2410116847 cites W2026131661 @default.
• W2410116847 cites W2026854879 @default.
• W2410116847 cites W2035026907 @default.
• W2410116847 cites W2038418568 @default.
• W2410116847 cites W2040647667 @default.
• W2410116847 cites W2042911972 @default.
• W2410116847 cites W2047431064 @default.
• W2410116847 cites W2053798949 @default.
• W2410116847 cites W2056470375 @default.
• W2410116847 cites W2059800482 @default.
• W2410116847 cites W2069315453 @default.
• W2410116847 cites W2072149257 @default.
• W2410116847 cites W2075266756 @default.
• W2410116847 cites W2087822830 @default.
• W2410116847 cites W2096987757 @default.
• W2410116847 cites W2098290011 @default.
• W2410116847 cites W2101674911 @default.
• W2410116847 cites W2106427050 @default.
• W2410116847 cites W2111072639 @default.
• W2410116847 cites W2122040390 @default.
• W2410116847 cites W2130378394 @default.
• W2410116847 cites W2141695047 @default.
• W2410116847 cites W2148446997 @default.
• W2410116847 cites W2153635508 @default.
• W2410116847 cites W2154568261 @default.
• W2410116847 cites W2154852616 @default.
• W2410116847 cites W2163683764 @default.
• W2410116847 cites W2166116275 @default.
• W2410116847 cites W2273476706 @default.
• W2410116847 cites W2326377633 @default.
• W2410116847 cites W23775767 @default.
• W2410116847 cites W4241607140 @default.
• W2410116847 doi "https://doi.org/10.1109/tcyb.2015.2458177" @default.
• W2410116847 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26259254" @default.
• W2410116847 hasPublicationYear "2016" @default.
• W2410116847 type Work @default.
• W2410116847 sameAs 2410116847 @default.
• W2410116847 citedByCount "40" @default.
• W2410116847 countsByYear W24101168472016 @default.
• W2410116847 countsByYear W24101168472017 @default.
• W2410116847 countsByYear W24101168472018 @default.
• W2410116847 countsByYear W24101168472019 @default.
• W2410116847 countsByYear W24101168472020 @default.
• W2410116847 countsByYear W24101168472021 @default.
• W2410116847 countsByYear W24101168472022 @default.
• W2410116847 countsByYear W24101168472023 @default.
• W2410116847 crossrefType "journal-article" @default.
• W2410116847 hasAuthorship W2410116847A5035486559 @default.
• W2410116847 hasAuthorship W2410116847A5040965135 @default.
• W2410116847 hasAuthorship W2410116847A5070629154 @default.
• W2410116847 hasConcept C11413529 @default.
• W2410116847 hasConcept C119857082 @default.
• W2410116847 hasConcept C121332964 @default.
• W2410116847 hasConcept C125453309 @default.
• W2410116847 hasConcept C13280743 @default.
• W2410116847 hasConcept C134306372 @default.
• W2410116847 hasConcept C138885662 @default.
• W2410116847 hasConcept C153180895 @default.
• W2410116847 hasConcept C154945302 @default.
• W2410116847 hasConcept C163258240 @default.
• W2410116847 hasConcept C164126121 @default.
• W2410116847 hasConcept C177148314 @default.
• W2410116847 hasConcept C185798385 @default.
• W2410116847 hasConcept C205649164 @default.
• W2410116847 hasConcept C27181475 @default.
• W2410116847 hasConcept C2776401178 @default.
• W2410116847 hasConcept C2780150128 @default.
• W2410116847 hasConcept C33923547 @default.
• W2410116847 hasConcept C41008148 @default.
• W2410116847 hasConcept C41895202 @default.
• W2410116847 hasConcept C43214815 @default.
• W2410116847 hasConcept C50644808 @default.
• W2410116847 hasConcept C62520636 @default.
• W2410116847 hasConceptScore W2410116847C11413529 @default.

• next