Inductive learning algorithms and representations for text categorization

By Shi Bing. 2006

675 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3a923a81a8f5069d50f827c22abe4546385603b4

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Citations in the corpus

Efficient phrase-based document indexing for Web document clustering (2004, methodology): “The methods used for text clustering includes decision trees [5], [6], [7], [8], statistical analysis [9], [10], [6], neural nets [11], inductive logic programming [12], [13], and rule-based systems [14], [15] among others.”
Comparison of Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification (2017, result): “In this paper, Naïve Bayes (Manning et al., 2008), Random Forest (Agrawal et al., 2013), Decision Tree (Rokach et al., 2005), Support Vector Machines (Flannery et al., 2007), and Logistic Regression (Caraciolo, 2011) classifiers are used to solve multi-class classification tasks.”
Self-taught hashing for fast similarity search (2010, background): “SVM in its simplest form, linear SVM f(x) = sgn(wx) consistently provides state-of-the-art performance for text classification tasks [10, 22, 49].”
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization (2009, unknown): “First, the two achieve top-notch performance among the widely used algorithms [10], [21], [22].”
Forecasting Intraday stock price trends with text mining techniques (2004, methodology): “Nevertheless, SVM is a very powerful method and has outperformed others in several studies [11][13] [14][15][16].”
An analysis of the relative hardness of Reuters-21578 subsets (2005, methodology): “Among others, this has been used in [2, 5, 9, 10, 25].”
Multilabel dimensionality reduction via dependence maximization (2008, methodology): “Using the “Bag-of-Words” representation [Dumais et al. 1998], Ueda and Saito [2003] tried to categorize real Web pages linked from the “yahoo.”
Feature selection for text categorization on imbalanced data (2004, background): “Attempts have been made to deal with this problem in diverse domains such as fraud detection [2], in-flight helicopter gearbox fault monitoring [4], and text categorization [6; 1; 9].”
A Brief Survey of Text Mining (2005, unknown): “Although this model is unrealistic due to its restrictive independence assumption it yields surprisingly good classiﬁcations [DPHS98, Joa98].”
A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C (2019, methodology): “7 For the textual data, we build bag-of-words support vector machines (SVM) with linear and RBF kernels, because they are well-established to be successful for text mining applications [23, 14, 31].”
Training linear SVMs in linear time (2006, background): “lems, for example, linear SVMs provide state-of-the-art prediction accuracy [10, 5, 17].”
TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY (2012, methodology): “Ci= α * centroid ci − β * centroid ~ci.[7] gives find similar method as of Rocchio is use in inductive learning process to find similarity between test example and category centroid using all feature .”
UMass at TREC 2004: Novelty and HARD (2004, unknown): “We used linear support vector machines (SVMs) as our classiﬁers because of their success at text classiﬁcation [32, 11, 8] and their ability to produce a ranking rather than merely a class prediction.”
Web site design with the patron in mind: A step-by-step guide for libraries (2006, methodology): “SVM classifiers were used because of its good performance in text/Web page classification [8, 11, 19].”
Managing computational complexity using surrogate models: a critical review (2020, methodology): “Inductive learning is a machine learning paradigm that is similar to metamodeling and regression (Dumais et al. 1998).”
Computational methods in authorship attribution (2009, unknown): “Comparative studies on machine learning methods for topic-based text categorization problems (Dumais et al. 1998; Yang 1999) have shown that in general, support vector machine (SVM) learning is at least as good for text categorization as any other learning method and the same has been found for…”
A k-nearest neighbor based algorithm for multi-label classification (2005, methodology): “One year later, using independent word-based Bag-ofWords representation [11], Ueda and Saito [3] presented two types of probabilistic generative models for multi-label text called parametric mixture models (PMM1, PMM2), where the basic assumption under PMMs is that multi-label text has a mixture of ”
Learning to detect malicious executables in the wild (2004, background): “Support vector machines (svms) [5] have performed well on traditional text classification tasks [12, 20, 36], and performed well on ours.”
Mining Meaning from Wikipedia (2008, unknown): “The ﬁrst entry is a baseline provided by Gabrilovich and Markovitch, which is in line with state-of-the-art methods such as Dumais et al. (1998).”