Kernel-Based Learning of Hierarchical Multilabel Classification Models

By Juho Rousu, Craig John Saunders, Sándor Szedmak, John Shawe-Taylor. 2006

306 citations recorded by the source corpus.

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Citations in the corpus

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs (2012, methodology): “As in our algorithm, Rousu et al. (2006) propose updating one training example at a time, but using multiple Frank-Wolfe updates to optimize along the subspace.”
Multi-Label Prediction via Compressed Sensing (2009, background): “class hierarchy), efficient learning and prediction methods are often possible [5, 6, 7, 8, 9].”
Efficient max-margin multi-label classification with applications to zero-shot learning (2012, methodology): “While the M3N formulation dealt with the Hamming loss, a more suitable hierarchical loss was introduced and efﬁciently optimised in Rousu et al. (2006) for the case of hierarchies.”
Multilabel relationship learning (2013, methodology): “Prior knowledge about relationships between labels may exist in some applications, and hierarchical multilabel learning methods [Cesa-Bianchi et al. 2006; Rousu et al. 2006; Vens et al. 2008; Hariharan et al. 2010] can utilize such prior knowledge.”
Multilabel classification via calibrated label ranking (2008, background): “Rousu et al. (2006) consider the hierarchical multilabel learning scenario, a combination of hierarchical classification (Cai and Hofmann 2004) and multilabel learning.”
A Review on Multi-Label Learning Algorithms (2014, background): “Furthermore, there are alternative multi-label settings where other than a single instance each example is represented by a bag of instances [201], [202] or graphs [87], [89], or extra ontology knowledge might exist on the label space such as hierarchy structure [2], [4], [14], [17], [124], [125], [”
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, background): “Hierarchies can make it easier to learn from a large number of labels [5, 12, 33].”
Hierarchical Bayesian Inference and Recursive Regularization for Large-Scale Classification (2015, methodology): “The more principled methods include the large-margin models by Tsochantaridis et al. [2006], Cai and Hofmann [2004], Rousu et al. [2006], Dekel et al. [2004], and to minimize a global loss over the hierarchy.”
Multi-label Learning with Missing Labels Using Mixed Dependency Graphs (2018, background): “Hierarchical multi-label learning (HML) [21] has been applied to problems where the label hierarchy exists, such as image annotation [38], text classification [33,34] and protein function prediction.”
Hierarchical Multi-Label Classification Networks (2018, unknown): “Typical HMC problems are text classification (Rousu et al., 2006; Cesa-Bianchi et al., 2006; Mayne & Perry, 2009; Lewis et al., 2004; Klimt & Yang, 2004), image annotation (Dimitrovski et al., 2011), and bioinformatics tasks such as protein function prediction (Otero et al., 2010; Valentini, 2011;…”
Recursive regularization for large-scale classification with hierarchical and graphical dependencies (2013, methodology): “The more principled methods include the large-margin models by [31],[6],[26],[9],[7] where the discriminant functions take the contributions from all nodes along the path to the root, and the model parameters are jointly learned to minimize a global loss over the hierarchy.”
Mining Multi-label Data (2010, methodology): “text categorization news article Reuters topics (agriculture, fishing) [16] web page Yahoo! directory (health, science) [17] patent WIPO (paper-making, fibreboard) [18, 19] email R&D activities (delegation) [20] legal document Eurovoc (software, copyright) [21] research article Heart conditions (myo”