Maximal Margin Labeling for Multi-Topic Text Categorization

By Hideto Kazawa, Tomonori Izumitani, Hirotoshi Taira, Eisaku Maeda. 2004

134 citations recorded by the source corpus.

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Citations in the corpus

SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (2019, methodology): “We will report to the participants all the measures and a summary of the performance in terms of macro-averaged F 1 - score, computed as follows: (5) Concerning the exact match, all the dimensions to be predicted will be jointly considered computing the Exact Match Ratio (Kazawa et al., 2005).”
Combining instance-based learning and logistic regression for multilabel classification (2009, background): “…has been developed, often motivated by speciﬁc types of applications such as text categorization [ Schapire and Singer, 2000; Ueda and Saito, 2003; Kazawa et al. , 2005; Zhang and Zhou, 2006], computer vision [Boutell et al. , 2004], and bioinformatics [Clare and King, 2001; Elis-seeff and…”
Feature selection for multi-label naive Bayes classification (2009, background): “[23] converted the original multi-label learning problem into a multi-class single-label one by treating a set of topics as a new class.”
Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora (2009, methodology): “As a baseline, we use a set of multiple one-vs-rest SVM classiﬁers which is a popular and extremely competitive baseline used by most previous papers (see (Kazawa et al., 2004; Ueda and Saito, 2003) for instance).”
Ieee Transactions on Knowledge and Data Engineering 1 Multi-label Neural Networks with Applications to Functional Genomics and Text Categorization (unknown): “As reviewed above, most of the existing multi-label learning algorithms are derived from traditional learning techniques such as probabilistic generative models [18], [30], boosting methods [28], decision trees [4], [5], and maximal margin methods [2], [8], [16].”
Multi-instance multi-label learning (2008, background): “Most studies on multi-label learning focus on text categorization [22,33,39,47,56, 65, 74], and several studies aim to improve the performance of text categorization systems by exploiting additional information given by the hierarchical structure of classes [14, 15, 53] or unlabeled data [43].”
A systematic analysis of performance measures for classification tasks (2009, methodology): “The quality of multi-topic classification (Table 4) is assessed through either partial or complete class label matching ( Kazawa, Izumitani, Taira, & Maeda, 2005 ); the latter is often referred to as exact matching.”
Hypergraph spectral learning for multi-label classification (2008, background): “yeast data set [9], and two high-dimensional document data sets [16, 21].”
A Review on Multi-Label Learning Algorithms (2014, background): “During the past decade, multi-label learning has gradually attracted significant attentions from machine learning and related communities and has been widely applied to diverse problems from automatic annotation for multimedia contents [5], [67], [74], [85], [102] to bioinformatics [16], [27], [107]”
Transductive Multilabel Learning via Label Set Propagation (2013, background): “…problem has been studied by a lot of researchers and many algorithms have been developed to solve different real-world application tasks, such as text categorization [8], [13], [20], [24], [28], [31], bioinformatics [10], [34], scene classification [4], image or video annotation [27].”
ML-KNN: A lazy learning approach to multi-label learning (2007, background): “Fortunately, several approaches specially designed for multi-label learning tasks have been proposed, such as multi-label text categorization algorithms [1,2,11,12], multilabel decision trees [5,6] and multi-label kernel methods [3,4] and multi-label neural networks [23].”