Matches in SemOpenAlex for { <https://semopenalex.org/work/W2461598051> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W2461598051 endingPage "983" @default.
- W2461598051 startingPage "979" @default.
- W2461598051 abstract "Abstract Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one." @default.
- W2461598051 created "2016-07-22" @default.
- W2461598051 creator A5000110032 @default.
- W2461598051 creator A5004016934 @default.
- W2461598051 creator A5010179903 @default.
- W2461598051 creator A5011811320 @default.
- W2461598051 creator A5020073188 @default.
- W2461598051 creator A5044748254 @default.
- W2461598051 creator A5071036387 @default.
- W2461598051 creator A5086385154 @default.
- W2461598051 date "2016-11-01" @default.
- W2461598051 modified "2023-10-16" @default.
- W2461598051 title "Automatic feature extraction in large fusion databases by using deep learning approach" @default.
- W2461598051 cites W2023086881 @default.
- W2461598051 cites W2102297952 @default.
- W2461598051 doi "https://doi.org/10.1016/j.fusengdes.2016.06.016" @default.
- W2461598051 hasPublicationYear "2016" @default.
- W2461598051 type Work @default.
- W2461598051 sameAs 2461598051 @default.
- W2461598051 citedByCount "25" @default.
- W2461598051 countsByYear W24615980512017 @default.
- W2461598051 countsByYear W24615980512018 @default.
- W2461598051 countsByYear W24615980512019 @default.
- W2461598051 countsByYear W24615980512020 @default.
- W2461598051 countsByYear W24615980512021 @default.
- W2461598051 countsByYear W24615980512022 @default.
- W2461598051 countsByYear W24615980512023 @default.
- W2461598051 crossrefType "journal-article" @default.
- W2461598051 hasAuthorship W2461598051A5000110032 @default.
- W2461598051 hasAuthorship W2461598051A5004016934 @default.
- W2461598051 hasAuthorship W2461598051A5010179903 @default.
- W2461598051 hasAuthorship W2461598051A5011811320 @default.
- W2461598051 hasAuthorship W2461598051A5020073188 @default.
- W2461598051 hasAuthorship W2461598051A5044748254 @default.
- W2461598051 hasAuthorship W2461598051A5071036387 @default.
- W2461598051 hasAuthorship W2461598051A5086385154 @default.
- W2461598051 hasConcept C124101348 @default.
- W2461598051 hasConcept C138885662 @default.
- W2461598051 hasConcept C153180895 @default.
- W2461598051 hasConcept C154945302 @default.
- W2461598051 hasConcept C158525013 @default.
- W2461598051 hasConcept C185592680 @default.
- W2461598051 hasConcept C2776401178 @default.
- W2461598051 hasConcept C41008148 @default.
- W2461598051 hasConcept C41895202 @default.
- W2461598051 hasConcept C43617362 @default.
- W2461598051 hasConcept C4725764 @default.
- W2461598051 hasConcept C52622490 @default.
- W2461598051 hasConcept C77088390 @default.
- W2461598051 hasConceptScore W2461598051C124101348 @default.
- W2461598051 hasConceptScore W2461598051C138885662 @default.
- W2461598051 hasConceptScore W2461598051C153180895 @default.
- W2461598051 hasConceptScore W2461598051C154945302 @default.
- W2461598051 hasConceptScore W2461598051C158525013 @default.
- W2461598051 hasConceptScore W2461598051C185592680 @default.
- W2461598051 hasConceptScore W2461598051C2776401178 @default.
- W2461598051 hasConceptScore W2461598051C41008148 @default.
- W2461598051 hasConceptScore W2461598051C41895202 @default.
- W2461598051 hasConceptScore W2461598051C43617362 @default.
- W2461598051 hasConceptScore W2461598051C4725764 @default.
- W2461598051 hasConceptScore W2461598051C52622490 @default.
- W2461598051 hasConceptScore W2461598051C77088390 @default.
- W2461598051 hasLocation W24615980511 @default.
- W2461598051 hasOpenAccess W2461598051 @default.
- W2461598051 hasPrimaryLocation W24615980511 @default.
- W2461598051 hasRelatedWork W1964120219 @default.
- W2461598051 hasRelatedWork W2016461833 @default.
- W2461598051 hasRelatedWork W2136054869 @default.
- W2461598051 hasRelatedWork W2144059113 @default.
- W2461598051 hasRelatedWork W2146076056 @default.
- W2461598051 hasRelatedWork W2363843671 @default.
- W2461598051 hasRelatedWork W2382607599 @default.
- W2461598051 hasRelatedWork W2811390910 @default.
- W2461598051 hasRelatedWork W3003836766 @default.
- W2461598051 hasRelatedWork W3197541072 @default.
- W2461598051 hasVolume "112" @default.
- W2461598051 isParatext "false" @default.
- W2461598051 isRetracted "false" @default.
- W2461598051 magId "2461598051" @default.
- W2461598051 workType "article" @default.