Abstract-Text classification is the process of inserting text into one or additional categories. Text categorization has many of significant application, Mostly in the field of organization, and for browsing within great groups of document. It is sometimes completed by means of "machine learning.". Since the system is built based on a wide range of document features."Feature selection." is an important approach within this process, since there are typically several thousand possible features terms. Within text categorization, The target goal of features selection is to improve the efficiency of procedures and reliability of classification by deleting features that have no relevance and non-essential terms. While keeping terms which hold enough data that facilitate with the classification task. The target goal of this work is to increase the efficient text categorization models. Within the "text mining" algorithms, a document is appearing as "vector" whose dimension is that the range of special keywords in it, which can be very large. Classic document categorization may be computationally costly. Therefore, feature extraction through the singular valued decomposition is employed for decrease the dimensionality of the documents, we are applying classification algorithms based on "Back propagation" and "Support Vector Machine." methodology. before the classification we applied "Principle Component Analysis." technique in order to improve the result accuracy . We then compared the performance of these two algorithms via computing standard precision and recall for the documentscollection.

Text classification is the process of inserting text into one or additional categories. Text categorization has many of significant application, Mostly in the field of organization, and for browsing within great groups of document. It is sometimes  completed by means of  "machine learning.". Since the system is built based on a wide range of document features."Feature selection." is an important approach within this process, since there are typically several thousand possible features terms. Within text categorization, The target goal of features selection is to improve the efficiency of procedures and reliability of classification by deleting features that have no relevance and non-essential terms. While keeping terms which hold enough data that facilitate with the classification task. The target goal of this work is to increase the efficient  text categorization models. Within  the "text mining" algorithms, a document is appearing as "vector" whose dimension is that the range of special keywords in it, which can be very large. Classic document categorization may be computationally costly. Therefore, feature extraction through the singular valued decomposition is employed for decrease the dimensionality of the documents, we are applying classification  algorithms based on "Back propagation" and "Support Vector Machine." methodology. before the classification we applied "Principle Component Analysis." technique  in order to improve  the result accuracy . We then compared  the performance  of these two algorithms via computing  standard precision and recall for the documentscollection.