conference · 2004
UN MODELO DE RECUPERACIÓN DE INFORMACIÓN BASADO EN SVMs INFORMATION RETRIEVAL MODEL BASED ON SVMS
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- TL;DR
- While Support Vector Machines (SVMs) have been used to organize documents efficiently, their effectiveness for document ranking in Information Retrieval had not yet been verified.
- Problem
- Not specified in the abstract.
- Method
- The paper introduces a transformation that maps the Information Retrieval process into a new vector space, where an SVM-based classifier is trained to learn the concept of document similarity.
- Results
- Not specified in the abstract.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- By transforming the retrieval process into a new vector space, the work applies SVM-based classification to learn document similarity.
- Applications
- Not specified in the abstract.
- Topics
- Information Retrieval, Support Vector Machines (SVMs), Vector Spaces, Document Similarity
- For industry
- Search and information retrieval.
- Why it matters
- Advances machine learning approaches for document ranking and similarity in search systems.
Abstract
Classifiers like the SVMs (Support Vector Machines) were used to organize documents very efficiently, but their effectiveness has not been verified in Information Retrieval for document hierarchizing. This paper proposes a transformation that associates the IR process to a new vectorial space where a SMVs-based classifier is trained to learn the concept of similarity with documents.