Quiver, provider of categorization software for enterprise and online content,
announced Version 2.0 of QKS Classifier, the most recent upgrade to its flagship taxonomy platform.
QKS Classifier is an end-to-end categorization application for organizing, managing and distributing unstructured data through the highest quality end user directories.
This updated release of QKS Classifier positions Quiver as the optimal choice for enterprises in need of a comprehensive and quickly deployable solution for organizing high-value information such as research, regulated or strategic content, says the company.
Version 2.0 upgrade introduces new features and functionality that uphold the Quiver objective of providing Global 2000 customers with the most accurate and flexible content categorization applications on the market.
Using Quiver’s QKS Classifier, enterprises operating in regulated or research environments are able to ensure that the right content is in the right place by providing an up-to-date and predictable topic tree or taxonomy for end user information access.
Among the new features of V 2.0
Multiple Categorization Techniques
- Support Vector Machine [SVM] Algorithm: In addition to the Bayesian engine in prior releases, this new content sorting algorithm utilizes linear pattern recognition in highly dimensional environments such as regulated or research content stores. The addition of SVM improves the accuracy of automated categorization results, and in conjunction with the new business rules engine provides unmatched accuracy in automated classification.
- Business Rules Engine: Powerful automated actions for publishing and expiration, offers additional control over categorization based on content source, document metadata, or confidence scores.
- End User Document Submission: Enlists end users in the process of building a community powered directory, by allowing submission of new content to specific topics, improves quality based on feedback regarding existing content, and increases taxonomy timeliness and user adoption.
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