New term weighting algorithm di yeturu jahnavi (7 risultati)

Condizione: New.

Taschenbuch. Condizione: 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.…

Condizione: 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.…

Condizione: New. Print on Demand.

Taschenbuch. Condizione: 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.…

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: 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.…