P02-1061 feature split displayed by the web page classification task studied in previous cotraining
W09-2204 settings can be distinguished for web page classification problems . On the one hand ,
W01-0501 contrast in two ways with the web page classification task studied in previous work
P10-1077 slack-rescaling for - mulation . For web page classification we will need fast processing
W01-0501 effectiveness of co-training on a web page classification task similar to that described
W09-2204 categories . In this work , we focus on web page classification based on Support Vector Machines
P03-1042 data to perform co-training and web page classification . The setting for the experiment
W09-2204 improve results for multiclass web page classification tasks using Support Vector Ma
W09-2204 ) . Analyzing the nature of a web page classification task , we can consider it to
C04-1187 Nevertheless , the performance of web page classification will influence the later clustering
C04-1187 formulation . Section 4 describes the web page classification and web document features used
P03-1042 Page Classification We used the web page classification data in Section 4.3 and conducted
D13-1162 Riloff and Lehnert , 1994 ) and web page classification ( Furnkranz et al. , 1998 ) .
W09-2204 organization . For this rea - son , web page classification has gained importance as a task
P03-1042 does not hold ) . Co-Training for Web Page Classification We used the web page classification
W01-0501 and Mitchell ( 1998 ) describe a web page classification task , in which the goal is to
W09-2204 classification , its application to the web page classification area remains without enough attention
P03-1042 with Theorem 2 . Co-Training for Web Page Classification We used the same data in ( Blum
W09-2201 promise in applications such as web page classification ( Blum and Mitchell , 1998 )
C04-1187 thousands to ten of thousands . 4 Web Page Classification In order to group the web pages
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