Matches in SemOpenAlex for { <https://semopenalex.org/work/W4206457784> ?p ?o ?g. }
- W4206457784 endingPage "54" @default.
- W4206457784 startingPage "5" @default.
- W4206457784 abstract "The Optimum-Path Forest (OPF) is a framework for the design of graph-based classifiers, which covers supervised, semisupervised, and unsupervised applications. The OPF is mainly characterized by its low training and classification times as well as competitive results against well-established machine learning techniques, such as Support Vector Machine and Artificial Neural Networks. Besides, the framework allows the design of different approaches based on the problem itself, which means a specific OPF-based classifier can be built for a given particular task. This paper surveyed several works published in the past years concerning OPF-based classifiers and sheds light on future trends concerning such a framework in the context of the deep learning era." @default.
- W4206457784 created "2022-01-25" @default.
- W4206457784 creator A5003275797 @default.
- W4206457784 creator A5015267493 @default.
- W4206457784 creator A5078890782 @default.
- W4206457784 date "2022-01-01" @default.
- W4206457784 modified "2023-10-16" @default.
- W4206457784 title "Theoretical background and related works" @default.
- W4206457784 cites W1583260003 @default.
- W4206457784 cites W1587083568 @default.
- W4206457784 cites W1964286771 @default.
- W4206457784 cites W1965154062 @default.
- W4206457784 cites W1970729013 @default.
- W4206457784 cites W1974330875 @default.
- W4206457784 cites W1980835351 @default.
- W4206457784 cites W1990517717 @default.
- W4206457784 cites W1994670735 @default.
- W4206457784 cites W1996655877 @default.
- W4206457784 cites W1996799478 @default.
- W4206457784 cites W2002460768 @default.
- W4206457784 cites W2004954930 @default.
- W4206457784 cites W2011430131 @default.
- W4206457784 cites W2012847678 @default.
- W4206457784 cites W2013504000 @default.
- W4206457784 cites W2024704261 @default.
- W4206457784 cites W2029943549 @default.
- W4206457784 cites W2030576407 @default.
- W4206457784 cites W2030670342 @default.
- W4206457784 cites W2044074081 @default.
- W4206457784 cites W2054145305 @default.
- W4206457784 cites W2056778909 @default.
- W4206457784 cites W2067016264 @default.
- W4206457784 cites W2070353586 @default.
- W4206457784 cites W2073683004 @default.
- W4206457784 cites W2080569508 @default.
- W4206457784 cites W2096411924 @default.
- W4206457784 cites W2099306544 @default.
- W4206457784 cites W2100199701 @default.
- W4206457784 cites W2114719076 @default.
- W4206457784 cites W2115245019 @default.
- W4206457784 cites W2115284428 @default.
- W4206457784 cites W2121947440 @default.
- W4206457784 cites W2122473047 @default.
- W4206457784 cites W2132549764 @default.
- W4206457784 cites W2158769954 @default.
- W4206457784 cites W2164780532 @default.
- W4206457784 cites W2169709581 @default.
- W4206457784 cites W2316804170 @default.
- W4206457784 cites W2330003016 @default.
- W4206457784 cites W2339019511 @default.
- W4206457784 cites W2398616336 @default.
- W4206457784 cites W2419654905 @default.
- W4206457784 cites W2439335642 @default.
- W4206457784 cites W2466038529 @default.
- W4206457784 cites W2492042711 @default.
- W4206457784 cites W2507652798 @default.
- W4206457784 cites W2512089339 @default.
- W4206457784 cites W2522347965 @default.
- W4206457784 cites W2579792291 @default.
- W4206457784 cites W2586433286 @default.
- W4206457784 cites W2622076177 @default.
- W4206457784 cites W2625255572 @default.
- W4206457784 cites W2730043722 @default.
- W4206457784 cites W2763912702 @default.
- W4206457784 cites W2764304448 @default.
- W4206457784 cites W2766113891 @default.
- W4206457784 cites W2773697307 @default.
- W4206457784 cites W2793050634 @default.
- W4206457784 cites W2793076225 @default.
- W4206457784 cites W2795559022 @default.
- W4206457784 cites W2811215564 @default.
- W4206457784 cites W2889744051 @default.
- W4206457784 cites W2898635601 @default.
- W4206457784 cites W2898936434 @default.
- W4206457784 cites W2903434143 @default.
- W4206457784 cites W2905188805 @default.
- W4206457784 cites W2909383866 @default.
- W4206457784 cites W2917962057 @default.
- W4206457784 cites W2921630855 @default.
- W4206457784 cites W2937034594 @default.
- W4206457784 cites W2937311542 @default.
- W4206457784 cites W2941861310 @default.
- W4206457784 cites W2944938295 @default.
- W4206457784 cites W2948437230 @default.
- W4206457784 cites W2960360130 @default.
- W4206457784 cites W2963572886 @default.
- W4206457784 cites W2966524519 @default.
- W4206457784 cites W2968718498 @default.
- W4206457784 cites W2992197405 @default.
- W4206457784 cites W2993774168 @default.
- W4206457784 cites W3008957426 @default.
- W4206457784 cites W3032668327 @default.
- W4206457784 cites W4248916828 @default.
- W4206457784 cites W4250320908 @default.
- W4206457784 cites W761992885 @default.
- W4206457784 cites W867138012 @default.
- W4206457784 doi "https://doi.org/10.1016/b978-0-12-822688-9.00010-4" @default.
- W4206457784 hasPublicationYear "2022" @default.