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9,465
One-Class SVMs for Document Classification
- Journal of Machine Learning Research
, 2001
"... We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one-class SV ..."
Abstract
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Cited by 185 (3 self)
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We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one
Text Classification from Labeled and Unlabeled Documents using EM
- MACHINE LEARNING
, 1999
"... This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large qua ..."
Abstract
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Cited by 1033 (15 self)
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This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large
Document Classification with LSA and
"... ABSTRACT. Latent semantic analysis is a computation method to demonstrate a major com-ponent of language learning and use. Thus, in this sense, it is a theory of meaning, such that it applies to and offers an explanation of phenomena of meaning in words and passages of words. This enables LSA to hol ..."
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to hold a strong position in the automated document classification, document analysis, etc. Though the experiments show that LSA can reach a very high accuracy in document classification, it also depends on the various factors such as quality and amount of training documents, characteristics
Knowledge Discovery for Document Classification
- AAAI-93 Workshop on Knowledge Discovery in Databases, Washington D.C., AAAI
, 1993
"... We report on extensive experiments using rule-based induction methods for document classification. The goal is to automatically discover patterns in document classifications, potentially surpassing humans who currently read and classify these documents. By using a decision rule model, we induce resu ..."
Abstract
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Cited by 1 (0 self)
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We report on extensive experiments using rule-based induction methods for document classification. The goal is to automatically discover patterns in document classifications, potentially surpassing humans who currently read and classify these documents. By using a decision rule model, we induce
Features for Unsupervised Document Classification
- Proceedings of CoNLL-2002
"... Unsupervised document classification is an important problem in practical text mining since training data is seldom available. In this paper we study the problem of term selection and the performance of various features for unsuper- vised text classification. The features studied are: principal comp ..."
Abstract
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Cited by 4 (0 self)
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Unsupervised document classification is an important problem in practical text mining since training data is seldom available. In this paper we study the problem of term selection and the performance of various features for unsuper- vised text classification. The features studied are: principal
Intelligent Document Classification
- Intelligent Data Analysis
, 2000
"... In this work we investigate some technical questions related to the application of neural networks in document classification. First, we discuss the effects of different averaging protocols for the 2 statistic used to remove non-informative terms. This is an especially relevant issue for the n ..."
Abstract
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Cited by 17 (6 self)
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In this work we investigate some technical questions related to the application of neural networks in document classification. First, we discuss the effects of different averaging protocols for the 2 statistic used to remove non-informative terms. This is an especially relevant issue
Abstract The tasks of document classification and
"... sentiment classification have been explored in the literature, but to our knowledge the task of political classification has not. We use a modified form of a document classification algorithm (Hu and Liu, 2004) to classify newspapers as liberal, conservative, or neutral based on their text. By using ..."
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sentiment classification have been explored in the literature, but to our knowledge the task of political classification has not. We use a modified form of a document classification algorithm (Hu and Liu, 2004) to classify newspapers as liberal, conservative, or neutral based on their text
Document Classification using Implication
"... Abstract: The document categorization is a very importatnt part of the current information retrieval systems for the WEB. The main problem of the text categorization is to find appropriate methods to manage the large amount of different words In this paper we describe a proposal for a theasurus-base ..."
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-based document classification. The graph edges are generated from the implication rules calculated witha frequent itemset uncovering algorithm. 1
Results 1 - 10
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9,465
