The (d, 1)-total number λ T d (G) of a graph G is the width of the smallest range of integers that suffices to label the vertices and the edges of G so that no two adjacent vertices have the same label, no two incident edges have the same label and the difference between the labels of a vertex and its incident edges is at least d. This notion was introduced in Havet. In this talk, we present ou...

We present an algorithm for unsupervised induction of labeled parse trees. The algorithm has three stages: bracketing, initial labeling, and label clustering. Bracketing is done from raw text using an unsupervised incremental parser. Initial labeling is done using a merging model that aims at minimizing the grammar description length. Finally, labels are clustered to a desired number of labels ...

Our aim is to produce a tessellation of space into small voxels and, based on only a few tomographic projections of an object, assign to each voxel a label from a small predetermined set that indicates one of the components of interest constituting the object. Traditional methods are not reliable due to, among other reasons, the low number of projections. We postulate a low level prior knowledg...

Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance further increased recently due to the growing need large-scale datasets train deep models. Weak could originate from multiple sources including non-expert annotators automatic labeling based on heuristics user interaction signals. There is extensive amo...

Multi-label text classification has been widely concerned by scholars due to its contribution practical applications. One of the key challenges in multi-label is how extract and leverage correlation among labels. However, it quite challenging directly model correlations labels a complex unknown label space. In this paper, we propose Label Prompt Text Classification (LP-MTC), which inspired idea...

Respiratory drugs are widely used in children to treat labeled and non-labeled indications but only some data are available quantifying comprehensively off-label usage. Thus, we aim to analyse drug utilisation and off-label prescribing of respiratory drugs focusing on age- and indication-related off-label use. Patients aged ≤18 years documented in the Bavarian Association of Statutory Health In...

A summary on the symbolic basic arithmetic operators and aggregation operators of linguistic information developed by the authors is presented. In particular, label addition, label diierence, product of a label by a positive real number, and convex combination of labels are shown as the symbolic basic arithmetic operators, and two aggregation operators of linguistic information built using thos...

A number of ensemble algorithms for solving multi-label classification problems have been proposed in recent years. Diversity among the base learners is known to be important for constructing a good ensemble. In this paper we define a method for introducing diversity among the base learners of one of the previously presented multi-label ensemble classifiers. An empirical comparison on 10 datase...

Web scale classification problems, such as Web page tagging and E-commerce product recommendation, are typically regarded as multi-label classification with an extremely large number of labels. In this paper, we propose GPT, which is a novel tree-based approach for extreme multi-label learning. GPT recursively splits a feature space with a hyperplane at each internal node, considering approxima...