Support vector machines classification with a very large-scale taxonomy

By Tie-Yan Liu, Yiming Yang, Hao Wan, Hua-Jun Zeng, Zheng Chen, Wei-Ying Ma. 2005

263 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN (2018, methodology): “We use the implementation of Hierarchical SVM (HSVM) [41] released by the authors and implement our version of Top-down Support Vector Machines (TD-SVM) [30].”
Large-Scale Multi-label Text Classification - Revisiting Neural Networks (2013, background): “However, their performances decrease as the number of labels grows and the label frequency distribution gets skewed [21, 27].”
Statistical topic models for multi-label document classification (2011, methodology): “34 the subset of labels which are leaves within the topic hierarchy (Lewis et al., 2004)—these automatically-assigned labels still play a major role during training.”
Hierarchical Bayesian Inference and Recursive Regularization for Large-Scale Classification (2015, background): “Directory have fewer than 5 positive instances [Liu et al. 2005], and 63 % of class labels in the English portion of Wikipedia have fewer than 10 positive instances.”
Predicting Pre-click Quality for Native Advertisements (2016, background): “Content YCT (text) [21] 21 Likelihood of the most top level YCT (Yahoo Category Taxonomy,”
Recursive regularization for large-scale classification with hierarchical and graphical dependencies (2013, background): “For example, 76% of the class-labels in the Yahoo! Directory have less than 5 positive instances [22] and 72% of the Open Directory Project have less than 4 positive instances (1).”
Bayesian models for Large-scale Hierarchical Classification (2012, background): “Some of the early works in HC [10, 14] use the hierarchical structure to decompose the classification problem into sub-problems recursively along the hierarchy and allocate a classifier at each node.”
Weakly-Supervised Hierarchical Text Classification (2018, methodology): “(Dumais and Chen 2000; Liu et al. 2005) propose to use local SVMs that are trained to distinguish the children classes of the same parent node so that the hierarchical classification task is decomposed into several flat classification tasks.”
Introduction to Information Retrieval (2008, unknown): “In a recent large study using the Yahoo! directory, Liu et al. (2005) conclude that hierarchical classification noticeably if still modestly outperforms flat classification.”