Matches in SemOpenAlex for { <https://semopenalex.org/work/W3161892633> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W3161892633 abstract "We propose a method attribute called Learning Frame Emotion Detection based on Hypergraph-based Support Vector Machine (SVM). A hypergraph is used in this HISVM to display various emotional relationships in the results. Then the topic of emotional forecasting is cast out as a regularized problem of Hypergraph cutting, where HISVM learns together a sequence of emotional forecasts from the functional space to a Hypergraph Embedded Vector Space that is harmonized with the different emotions. The predictions learned was directly categorized by emotion. This is a compelling strategy-this formulation. Our proposed algorithm will implement other available details, including class labels, flexibly by treating our model as a multifaceted cutting job. Hypergraph Optimized Vector Assistance Machine, we extend our approach to emotion identification. As a significant preprocessing level, selection features are thought to define the most distinctive functions for a compact statistical analysis in many machine learning and model recognition fields. Then we propose a regularized word, a regularized Hypergraph SVM algorithm. We perform tests on various data sets from SVM's machine learning algorithm and two text-based expression grading projects in the real world. Experimental findings show the proposed process's positive results compared to many standard selection methods for emotional characteristics." @default.
- W3161892633 created "2021-05-24" @default.
- W3161892633 creator A5051886730 @default.
- W3161892633 creator A5071079054 @default.
- W3161892633 date "2021-04-14" @default.
- W3161892633 modified "2023-09-26" @default.
- W3161892633 title "Hypergraph Regularized SVM and Its Application Emotion Detection" @default.
- W3161892633 cites W1523985187 @default.
- W3161892633 cites W1548832181 @default.
- W3161892633 cites W1629199664 @default.
- W3161892633 cites W1984195582 @default.
- W3161892633 cites W2028633780 @default.
- W3161892633 cites W2036303021 @default.
- W3161892633 cites W2045620328 @default.
- W3161892633 cites W2052684427 @default.
- W3161892633 cites W2093126287 @default.
- W3161892633 cites W2116063398 @default.
- W3161892633 cites W2407353737 @default.
- W3161892633 cites W2466704481 @default.
- W3161892633 cites W2578341372 @default.
- W3161892633 cites W2724710774 @default.
- W3161892633 cites W3122665668 @default.
- W3161892633 cites W43954826 @default.
- W3161892633 cites W967748955 @default.
- W3161892633 doi "https://doi.org/10.1109/ipec51340.2021.9421069" @default.
- W3161892633 hasPublicationYear "2021" @default.
- W3161892633 type Work @default.
- W3161892633 sameAs 3161892633 @default.
- W3161892633 citedByCount "1" @default.
- W3161892633 countsByYear W31618926332023 @default.
- W3161892633 crossrefType "proceedings-article" @default.
- W3161892633 hasAuthorship W3161892633A5051886730 @default.
- W3161892633 hasAuthorship W3161892633A5071079054 @default.
- W3161892633 hasConcept C10551718 @default.
- W3161892633 hasConcept C11413529 @default.
- W3161892633 hasConcept C118615104 @default.
- W3161892633 hasConcept C119857082 @default.
- W3161892633 hasConcept C12267149 @default.
- W3161892633 hasConcept C124101348 @default.
- W3161892633 hasConcept C148483581 @default.
- W3161892633 hasConcept C153180895 @default.
- W3161892633 hasConcept C154945302 @default.
- W3161892633 hasConcept C2777212361 @default.
- W3161892633 hasConcept C2781221856 @default.
- W3161892633 hasConcept C33923547 @default.
- W3161892633 hasConcept C34736171 @default.
- W3161892633 hasConcept C41008148 @default.
- W3161892633 hasConceptScore W3161892633C10551718 @default.
- W3161892633 hasConceptScore W3161892633C11413529 @default.
- W3161892633 hasConceptScore W3161892633C118615104 @default.
- W3161892633 hasConceptScore W3161892633C119857082 @default.
- W3161892633 hasConceptScore W3161892633C12267149 @default.
- W3161892633 hasConceptScore W3161892633C124101348 @default.
- W3161892633 hasConceptScore W3161892633C148483581 @default.
- W3161892633 hasConceptScore W3161892633C153180895 @default.
- W3161892633 hasConceptScore W3161892633C154945302 @default.
- W3161892633 hasConceptScore W3161892633C2777212361 @default.
- W3161892633 hasConceptScore W3161892633C2781221856 @default.
- W3161892633 hasConceptScore W3161892633C33923547 @default.
- W3161892633 hasConceptScore W3161892633C34736171 @default.
- W3161892633 hasConceptScore W3161892633C41008148 @default.
- W3161892633 hasLocation W31618926331 @default.
- W3161892633 hasOpenAccess W3161892633 @default.
- W3161892633 hasPrimaryLocation W31618926331 @default.
- W3161892633 hasRelatedWork W2126100045 @default.
- W3161892633 hasRelatedWork W2296226123 @default.
- W3161892633 hasRelatedWork W2354796444 @default.
- W3161892633 hasRelatedWork W2378657478 @default.
- W3161892633 hasRelatedWork W2953978304 @default.
- W3161892633 hasRelatedWork W2980284037 @default.
- W3161892633 hasRelatedWork W3105251098 @default.
- W3161892633 hasRelatedWork W3161892633 @default.
- W3161892633 hasRelatedWork W3162160273 @default.
- W3161892633 hasRelatedWork W2345184372 @default.
- W3161892633 isParatext "false" @default.
- W3161892633 isRetracted "false" @default.
- W3161892633 magId "3161892633" @default.
- W3161892633 workType "article" @default.