Model for Information Extraction and Information Retrieval Based on Cascaded Support Vector Machine and Feature Vector Optimization

Computer Engineering

Information extraction and optimization play vital role in semantic web mining. Semantic web mining process proceeds the better way information retrieval over the ocean of internet database. In this paper design model of information optimization using feature vector optimization. The feature vector optimization process reduces the relational key constraints for the categorization of information for retrieval. Relative information categorization increases the hit ratio of retrieving data. For the better categorization of relative information used cascaded support vector machine. The cascaded support vector machine is two stages multi-kernel-based classification process. The two-stage support vector machine reduces the semantic gap between the search query and internet database. The design model removes the bottleneck of relational constraints of the post mining process. The feature vector optimization is used to find optimal attribute for the label of the cascaded support vector machine. The labeled class of support vector machine map the domain ontology database

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