A Statistical Learning Model of Text Classification for Support Vector Machines.

By Thorsten Joachims. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001

299 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a2c59d8d04d6ff79de00591245a6fb5fc3be154b

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Citations in the corpus

Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003. (2002, unknown): “An interesting revelation made by SVMs is that many standard text classification tasks have classes that are perfectly or near-perfectly separable using a hyperplane in feature space [114].”
Information gain and divergence-based feature selection for machine learning-based text categorization (2006, methodology): “Another major characteristic of text categorization problems is the high level of feature redundancy (Joachims, 2001).”
A Survey of Text Classification Algorithms (2012, methodology): “A deeper theoretical study of the SVM method has been provided in [67].”
A Survey on Text Classification: From Traditional to Deep Learning (2020, methodology): “To analyze what the SVM algorithms learn and what tasks are suitable, Joachims [47] proposes a theoretical learning model combining the statistical traits with the generalization performance of an SVM, analyzing the features and benefits using a quantitative approach.”
An kNN Model-Based Approach and Its Application in Text Categorization (2004, methodology): “This includes Naïve Bayes (NB) probabilistic classifiers [2], Decision Tree classifiers [3], Decision Rules [4], regression methods [5], Neural Network [6], k-NN classifiers [5, 7], Support Vector Machine (SVMs) [8, 9], and Rocchio classifiers [10, 11] etc.”
Automatic document metadata extraction using support vector machines (2003, methodology): “Encouraged by their success in handling high dimensional feature spaces for classification problems [ 12 , 9], we investigate Support Vector Machines (SVMs) for metadata extraction.”
Learning to Decode Cognitive States from Brain Images (2004, methodology): “For example, Naive Bayes classifiers, kNN, and SVM have all been used for text classification problems (Nigam et al., 2000; Joachims, 2001; Yang, 1999), where the dimension of the data is approximately 105, corresponding to the size of the natural language vocabulary.”
Distributional Word Clusters vs. Words for Text Categorization (2003, methodology): “…it is worth mentioning the empirical study by Yang and Liu (1999) (who showed that SVM outperforms other classifiers, including kNN and Naive Bayes, on Reuters with both large and small training sets) and the theoretical account of Joachims (2001) for the suitability of SVM for text categorization.”
A Survey on Text Classification: From Shallow to Deep Learning (2020, methodology): “To analyze what the SVM algorithms learn and what tasks are suitable, Joachims [108] proposes a theoretical learning model combining the statistical traits with the generalization performance of an SVM analyzing the features and benefits using a quantitative approach.”