p vijayaragavan (57 risultati)

Paperback. Condizione: new. Paperback. This study presents a hybrid model that leverages the strengths of K-means clustering and Support Vector Machines (SVM) for classifying online product reviews. K-means is used to group reviews into clusters, reducing data complexity and improving feature extraction. Subsequently, SVM is employed to classify the clustered data into positive, negative, or neutral sentiments. The combined approach enhances classification accuracy, reduces computational cost, and effectively handles large datasets. Experimental results demonstrate that the proposed model outperforms traditional standalone classifiers in terms of precision, recall, and overall accuracy. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: New.

Condizione: New. In.

Paperback. Condizione: new. Paperback. This study presents a hybrid model that leverages the strengths of K-means clustering and Support Vector Machines (SVM) for classifying online product reviews. K-means is used to group reviews into clusters, reducing data complexity and improving feature extraction. Subsequently, SVM is employed to classify the clustered data into positive, negative, or neutral sentiments. The combined approach enhances classification accuracy, reduces computational cost, and effectively handles large datasets. Experimental results demonstrate that the proposed model outperforms traditional standalone classifiers in terms of precision, recall, and overall accuracy. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condizione: new. Paperback. This study presents a hybrid model that leverages the strengths of K-means clustering and Support Vector Machines (SVM) for classifying online product reviews. K-means is used to group reviews into clusters, reducing data complexity and improving feature extraction. Subsequently, SVM is employed to classify the clustered data into positive, negative, or neutral sentiments. The combined approach enhances classification accuracy, reduces computational cost, and effectively handles large datasets. Experimental results demonstrate that the proposed model outperforms traditional standalone classifiers in terms of precision, recall, and overall accuracy. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condizione: Neu. Neuware -This study presents a hybrid model that leverages the strengths of K-means clustering and Support Vector Machines (SVM) for classifying online product reviews. K-means is used to group reviews into clusters, reducing data complexity and improving feature extraction. Subsequently, SVM is employed to classify the clustered data into positive, negative, or neutral sentiments. The combined approach enhances classification accuracy, reduces computational cost, and effectively handles large datasets. Experimental results demonstrate that the proposed model outperforms traditional standalone classifiers in terms of precision, recall, and overall accuracy.Books on Demand GmbH, Überseering 33, 22297 Hamburg 172 pp. Englisch.

Paperback. Condizione: new. Paperback. Cette etude presente un modele hybride qui exploite les forces du regroupement K-means et des machines a vecteurs de support (SVM) pour classer les critiques de produits en ligne. K-means est utilise pour regrouper les avis en grappes, ce qui reduit la complexite des donnees et ameliore l'extraction des caracteristiques. Ensuite, les SVM sont utilises pour classer les donnees regroupees en sentiments positifs, negatifs ou neutres. L'approche combinee ameliore la precision de la classification, reduit les couts de calcul et traite efficacement les grands ensembles de donnees. Les resultats experimentaux demontrent que le modele propose est plus performant que les classificateurs traditionnels autonomes en termes de precision, de rappel et de precision globale. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condizione: Neu. Neuware -Cette étude présente un modèle hybride qui exploite les forces du regroupement K-means et des machines à vecteurs de support (SVM) pour classer les critiques de produits en ligne. K-means est utilisé pour regrouper les avis en grappes, ce qui réduit la complexité des données et améliore l'extraction des caractéristiques. Ensuite, les SVM sont utilisés pour classer les données regroupées en sentiments positifs, négatifs ou neutres. L'approche combinée améliore la précision de la classification, réduit les coûts de calcul et traite efficacement les grands ensembles de données. Les résultats expérimentaux démontrent que le modèle proposé est plus performant que les classificateurs traditionnels autonomes en termes de précision, de rappel et de précision globale.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Französisch.

Condizione: New.

Condizione: New.

Paperback. Condizione: new. Paperback. Questo studio presenta un modello ibrido che sfrutta i punti di forza del clustering K-means e delle macchine vettoriali di supporto (SVM) per classificare le recensioni di prodotti online. K-means viene utilizzato per raggruppare le recensioni in cluster, riducendo la complessita dei dati e migliorando l'estrazione delle caratteristiche. Successivamente, si ricorre a SVM per classificare i dati raggruppati in sentimenti positivi, negativi o neutri. L'approccio combinato migliora l'accuratezza della classificazione, riduce il costo computazionale e gestisce efficacemente grandi insiemi di dati. I risultati sperimentali dimostrano che il modello proposto supera i classificatori tradizionali indipendenti in termini di precisione, richiamo e accuratezza complessiva. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condizione: new. Paperback. Niniejsze badanie przedstawia model hybrydowy, ktory wykorzystuje mocne strony klasteryzacji K-srednich i maszyn wektorow nosnych (SVM) do klasyfikacji recenzji produktow online. K-srednich sluzy do grupowania recenzji w klastry, zmniejszajac zlozonosc danych i poprawiajac ekstrakcje cech. Nastepnie SVM jest wykorzystywana do klasyfikowania danych w klastrach na pozytywne, negatywne lub neutralne nastroje. Polaczone podejscie zwieksza dokladnosc klasyfikacji, zmniejsza koszty obliczeniowe i skutecznie obsluguje duze zbiory danych. Wyniki eksperymentalne pokazuja, ze proponowany model przewyzsza tradycyjne samodzielne klasyfikatory pod wzgledem precyzji, przywolania i ogolnej dokladnosci. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condizione: new. Paperback. In dieser Studie wird ein hybrides Modell vorgestellt, das die Staerken von K-means Clustering und Support Vector Machines (SVM) zur Klassifizierung von Online-Produktbewertungen nutzt. K-means wird verwendet, um Bewertungen in Clustern zu gruppieren, wodurch die Datenkomplexitaet reduziert und die Merkmalsextraktion verbessert wird. Anschliessend wird SVM eingesetzt, um die geclusterten Daten in positive, negative oder neutrale Bewertungen zu klassifizieren. Der kombinierte Ansatz verbessert die Klassifizierungsgenauigkeit, reduziert die Rechenkosten und bewaeltigt effektiv grosse Datensaetze. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene Modell herkoemmliche eigenstaendige Klassifizierungsverfahren in Bezug auf Praezision, Wiedererkennung und Gesamtgenauigkeit uebertrifft. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condizione: new. Paperback. Este estudo apresenta um modelo hibrido que aproveita os pontos fortes do agrupamento K-means e das Maquinas de Vectores de Suporte (SVM) para classificar as avaliacoes de produtos online. O K-means e utilizado para agrupar as criticas em clusters, reduzindo a complexidade dos dados e melhorando a extracao de carateristicas. Subsequentemente, a SVM e utilizada para classificar os dados agrupados em sentimentos positivos, negativos ou neutros. A abordagem combinada aumenta a precisao da classificacao, reduz o custo computacional e lida eficazmente com grandes conjuntos de dados. Os resultados experimentais demonstram que o modelo proposto supera os classificadores autonomos tradicionais em termos de precisao, recuperacao e exatidao global. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condizione: Neu. Neuware -Questo studio presenta un modello ibrido che sfrutta i punti di forza del clustering K-means e delle macchine vettoriali di supporto (SVM) per classificare le recensioni di prodotti online. K-means viene utilizzato per raggruppare le recensioni in cluster, riducendo la complessità dei dati e migliorando l'estrazione delle caratteristiche. Successivamente, si ricorre a SVM per classificare i dati raggruppati in sentimenti positivi, negativi o neutri. L'approccio combinato migliora l'accuratezza della classificazione, riduce il costo computazionale e gestisce efficacemente grandi insiemi di dati. I risultati sperimentali dimostrano che il modello proposto supera i classificatori tradizionali indipendenti in termini di precisione, richiamo e accuratezza complessiva.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 200 pp. Italienisch.

Taschenbuch. Condizione: Neu. Neuware -In dieser Studie wird ein hybrides Modell vorgestellt, das die Stärken von K-means Clustering und Support Vector Machines (SVM) zur Klassifizierung von Online-Produktbewertungen nutzt. K-means wird verwendet, um Bewertungen in Clustern zu gruppieren, wodurch die Datenkomplexität reduziert und die Merkmalsextraktion verbessert wird. Anschließend wird SVM eingesetzt, um die geclusterten Daten in positive, negative oder neutrale Bewertungen zu klassifizieren. Der kombinierte Ansatz verbessert die Klassifizierungsgenauigkeit, reduziert die Rechenkosten und bewältigt effektiv große Datensätze. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene Modell herkömmliche eigenständige Klassifizierungsverfahren in Bezug auf Präzision, Wiedererkennung und Gesamtgenauigkeit übertrifft.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Deutsch.

Taschenbuch. Condizione: Neu. Neuware -Niniejsze badanie przedstawia model hybrydowy, który wykorzystuje mocne strony klasteryzacji K-¿rednich i maszyn wektorów nönych (SVM) do klasyfikacji recenzji produktów online. K-¿rednich s¿u¿y do grupowania recenzji w klastry, zmniejszaj¿c z¿öonö¿ danych i poprawiaj¿c ekstrakcj¿ cech. Nast¿pnie SVM jest wykorzystywana do klasyfikowania danych w klastrach na pozytywne, negatywne lub neutralne nastroje. Pö¿czone podej¿cie zwi¿ksza dok¿adnö¿ klasyfikacji, zmniejsza koszty obliczeniowe i skutecznie obs¿uguje du¿e zbiory danych. Wyniki eksperymentalne pokazuj¿, ¿e proponowany model przewy¿sza tradycyjne samodzielne klasyfikatory pod wzgl¿dem precyzji, przywöania i ogólnej dok¿adnöci.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 200 pp. Polnisch.

Condizione: New.

Condizione: New.

Condizione: New.

Taschenbuch. Condizione: Neu. Angewandtes maschinelles Lernen | Ein effizientes Clustering-basiertes Klassifizierungsmodell für Online-Produktbewertungen unter Verwendung von Support-Vektor-Maschinen und K-means A | Vijayaragavan P | Taschenbuch | 208 S. | Deutsch | 2025 | Verlag Unser Wissen | EAN 9786208796723 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 172 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 204 pp. Spanisch.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Este estudio presenta un modelo híbrido que aprovecha los puntos fuertes de la agrupación K-means y las máquinas de vectores de soporte (SVM) para clasificar las reseñas de productos en línea. K-means se utiliza para agrupar las reseñas en clusters, reduciendo la complejidad de los datos y mejorando la extracción de características. Posteriormente, se emplea SVM para clasificar los datos agrupados en sentimientos positivos, negativos o neutros. El enfoque combinado mejora la precisión de la clasificación, reduce el coste computacional y gestiona eficazmente grandes conjuntos de datos. Los resultados experimentales demuestran que el modelo propuesto supera a los clasificadores independientes tradicionales en términos de precisión, recuperación y exactitud general.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 204 pp. Spanisch.