The nature of statistical learning theory
The nature of statistical learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Making large-scale support vector machine learning practical
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Pairwise Classification as an Ensemble Technique
ECML '02 Proceedings of the 13th European Conference on Machine Learning
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Exploiting extremely rare features in text categorization
ECML'06 Proceedings of the 17th European conference on Machine Learning
On the behavior of SVM and some older algorithms in binary text classification tasks
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Binarized Support Vector Machines
INFORMS Journal on Computing
Document-level sentiment classification: An empirical comparison between SVM and ANN
Expert Systems with Applications: An International Journal
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We are concerned with the problem of learning classification rules in text categorization where many authors presented Support Vector Machines (SVM) as leading classification method. Number of studies, however, repeatedly pointed out that in some situations SVM is outperformed by simpler methods such as naive Bayes or nearest-neighbor rule. In this paper, we aim at developing better understanding of SVM behaviour in typical text categorization problems represented by sparse bag of words feature spaces. We study in details the performance and the number of support vectors when varying the training set size, the number of features and, unlike existing studies, also SVM free parameter C, which is the Lagrange multipliers upper bound in SVM dual. We show that SVM solutions with small C are high performers. However, most training documents are then bounded support vectors sharing a same weight C. Thus, SVM reduce to a nearest mean classifier; this raises an interesting question on SVM merits in sparse bag of words feature spaces. Additionally, SVM suffer from performance deterioration for particular training set size/number of features combinations.