The document discusses using support vector machines (SVM) for automatic document categorization. It proposes using an SVM trained on a collection of documents that have been manually categorized into fields and groups. Documents are represented as sparse vectors of words and their TF-IDF weights. An SVM is trained for each category on a subset of documents. The trained SVMs are then used to categorize new documents by predicting the likelihood they belong to each category. The method achieved good recall and precision on test documents from several sample categories. Improvements and future work expanding the approach are also discussed.
This document describes a rough set theory approach to text categorization. It discusses text representation techniques like tokenization, vector space models, stop word removal and stemming. It then introduces rough set theory concepts like lower and upper approximations that are used to classify texts. The algorithm takes documents as input, processes them, builds a classifier using rough sets, and evaluates performance based on accuracy, error rate, precision and recall.
The document discusses using support vector machines (SVM) for automatic document categorization. It proposes using an SVM trained on a collection of documents that have been manually categorized into fields and groups. Documents are represented as sparse vectors of words and their TF-IDF weights. An SVM is trained for each category on a subset of documents. The trained SVMs are then used to categorize new documents by predicting the likelihood they belong to each category. The method achieved good recall and precision on test documents from several sample categories. Improvements and future work expanding the approach are also discussed.
This document describes a rough set theory approach to text categorization. It discusses text representation techniques like tokenization, vector space models, stop word removal and stemming. It then introduces rough set theory concepts like lower and upper approximations that are used to classify texts. The algorithm takes documents as input, processes them, builds a classifier using rough sets, and evaluates performance based on accuracy, error rate, precision and recall.
O Pebble Beach Concours d'Elegance é um concurso de carros clássicos onde apenas veículos totalmente originais e em perfeito estado podem competir. Os milionários de todo o mundo levam seus carros mais raros e valiosos para exibir em uma competição onde os detalhes mais minúsculos são inspecionados.
O Pebble Beach Concours d'Elegance é um concurso de carros clássicos onde apenas veículos totalmente originais e em perfeito estado podem competir. Os milionários de todo o mundo levam seus carros mais raros e valiosos para exibir em uma competição onde os detalhes mais minúsculos são inspecionados.