Articles liés à A New Term Weighting Algorithm for Identifying Salient...
Résultats de recherche pour A New Term Weighting Algorithm for Identifying Salient...
Image fournie par le vendeur
A New Term Weighting Algorithm for Identifying Salient Events
Edité par
LAP LAMBERT Academic Publishing Feb 2018, 2018
ISBN 10 : 6136876388
ISBN 13 : 9786136876382
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Term weighting is a useful technique that extracts important features from textual documents, thereby providing a basis for different Text Mining approaches. The objective of this work is to study the existing term weighting algorithms for feature extraction and to develop an efficient term weighting algorithm for mining salient features from internet based newswire sources. TF\*PDF (Term Frequency \* Proportional Document Frequency) is the most popular term weighting algorithm which extracts influential features from news archives. TF\*PDF satisfies the basic property of the features in news documents i.e., frequency and thus increases the accuracy when compared to other term weighing algorithms such as Binary, TF (Term Frequency), TF-IDF (Term Frequency-Inverse Document Frequency) and its variants. However, only frequency property is not sufficient for salient topic extraction. To overcome that problem, this book presents an innovative and effective term weighting algorithm that considers Position, Scattering and Topicality along with Frequency for extracting short lived and long running events. 136 pp. Englisch. N° de réf. du vendeur 9786136876382
Acheter neuf
EUR 55,90
Expédition à EUR 23
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
A New Term Weighting Algorithm for Identifying Salient Events
Edité par
LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6136876388
ISBN 13 : 9786136876382
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yeturu JahnaviY. Jahnavi received her BTech from JNTU, MTech from Sri Venkateswara Univeristy, Tirupathi and PhD from GITAM,Visakhapatnam. She qualified GATE, APSET and NET examinations. She is a member of CSI, LMISTE etc. She is cur. N° de réf. du vendeur 508183604
Acheter neuf
EUR 46,18
Expédition à EUR 48,99
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
A New Term Weighting Algorithm for Identifying Salient Events
Edité par
LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6136876388
ISBN 13 : 9786136876382
Neuf
Couverture souple
Vendeur : Books Puddle, Woodside, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 26397275475
Acheter neuf
EUR 92,08
Expédition à EUR 3,58
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
A New Term Weighting Algorithm for Identifying Salient Events
Edité par
LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6136876388
ISBN 13 : 9786136876382
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Term weighting is a useful technique that extracts important features from textual documents, thereby providing a basis for different Text Mining approaches. The objective of this work is to study the existing term weighting algorithms for feature extraction and to develop an efficient term weighting algorithm for mining salient features from internet based newswire sources. TF\*PDF (Term Frequency \* Proportional Document Frequency) is the most popular term weighting algorithm which extracts influential features from news archives. TF\*PDF satisfies the basic property of the features in news documents i.e., frequency and thus increases the accuracy when compared to other term weighing algorithms such as Binary, TF (Term Frequency), TF-IDF (Term Frequency-Inverse Document Frequency) and its variants. However, only frequency property is not sufficient for salient topic extraction. To overcome that problem, this book presents an innovative and effective term weighting algorithm that considers Position, Scattering and Topicality along with Frequency for extracting short lived and long running events. N° de réf. du vendeur 9786136876382
Acheter neuf
EUR 63,47
Expédition à EUR 35
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
A New Term Weighting Algorithm for Identifying Salient Events
Edité par
LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6136876388
ISBN 13 : 9786136876382
Neuf
Couverture souple
Vendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Print on Demand. N° de réf. du vendeur 400182924
Acheter neuf
EUR 94,06
Expédition à EUR 7,79
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image d'archives
A New Term Weighting Algorithm for Identifying Salient Events
Edité par
LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6136876388
ISBN 13 : 9786136876382
Neuf
Couverture souple
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18397275481
Acheter neuf
EUR 94,08
Expédition à EUR 9,95
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
A New Term Weighting Algorithm for Identifying Salient Events
Edité par
LAP LAMBERT Academic Publishing Feb 2018, 2018
ISBN 10 : 6136876388
ISBN 13 : 9786136876382
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Term weighting is a useful technique that extracts important features from textual documents, thereby providing a basis for different Text Mining approaches. The objective of this work is to study the existing term weighting algorithms for feature extraction and to develop an efficient term weighting algorithm for mining salient features from internet based newswire sources. TF\*PDF (Term Frequency \* Proportional Document Frequency) is the most popular term weighting algorithm which extracts influential features from news archives. TF\*PDF satisfies the basic property of the features in news documents i.e., frequency and thus increases the accuracy when compared to other term weighing algorithms such as Binary, TF (Term Frequency), TF-IDF (Term Frequency-Inverse Document Frequency) and its variants. However, only frequency property is not sufficient for salient topic extraction. To overcome that problem, this book presents an innovative and effective term weighting algorithm that considers Position, Scattering and Topicality along with Frequency for extracting short lived and long running events.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch. N° de réf. du vendeur 9786136876382
Acheter neuf
EUR 55,90
Expédition à EUR 60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)