We have tested this by developing a small prototype that used thecorpora of labeled documents with some different learning algorithmsto see if the results would be satisfactory. We conclude that while thesystem would indeed make it easier for someone to classify unlabeleddocuments, it can not work totally autonomously based on the rela-tively small amount of documents and large amount of categories thatare in the ontology
Abstract: Data mining extracts novel and useful knowledge from large repositories of data and has be...
We present and analyze a theoretical model designed to understand and explain the ef-fectiveness of ...
An important task of information retrieval is to induce classifiers capable of categorizing text doc...
This paper explores the use of a machine learning algorithm to automate the task of classifying lear...
Because of the explosion of digital and online text information, automatic organization of documents...
For the past few years, text categorization has emerged as an application domain to machine learn-in...
The automated categorization (or classification) of texts into predefined categories has witnessed a...
This paper examines the use of inductive learning to categorize natural language documents into pred...
Hierarchical supervised classifiers are highly demanding in terms of labelled examples, because the...
In many important text classification problems, acquiring class labels for training documents is cos...
We present and analyze a theoretical model designed to understand and explain the effectiveness of o...
Exponential growth rates of learning materials and rapid distribution of those resources among e-lea...
Master of ScienceDepartment of Computer ScienceWilliam HsuThis work describes a comparative study of...
With the rapid growth of online documents available on the World Wide Web necessitate the task of cl...
document are those of the author and should not be interpreted as representing the official policies...
Abstract: Data mining extracts novel and useful knowledge from large repositories of data and has be...
We present and analyze a theoretical model designed to understand and explain the ef-fectiveness of ...
An important task of information retrieval is to induce classifiers capable of categorizing text doc...
This paper explores the use of a machine learning algorithm to automate the task of classifying lear...
Because of the explosion of digital and online text information, automatic organization of documents...
For the past few years, text categorization has emerged as an application domain to machine learn-in...
The automated categorization (or classification) of texts into predefined categories has witnessed a...
This paper examines the use of inductive learning to categorize natural language documents into pred...
Hierarchical supervised classifiers are highly demanding in terms of labelled examples, because the...
In many important text classification problems, acquiring class labels for training documents is cos...
We present and analyze a theoretical model designed to understand and explain the effectiveness of o...
Exponential growth rates of learning materials and rapid distribution of those resources among e-lea...
Master of ScienceDepartment of Computer ScienceWilliam HsuThis work describes a comparative study of...
With the rapid growth of online documents available on the World Wide Web necessitate the task of cl...
document are those of the author and should not be interpreted as representing the official policies...
Abstract: Data mining extracts novel and useful knowledge from large repositories of data and has be...
We present and analyze a theoretical model designed to understand and explain the ef-fectiveness of ...
An important task of information retrieval is to induce classifiers capable of categorizing text doc...