Probabilistic Approach for Multi-label Classification, M.Sc. Thesis Sharif University of Technology ; Soleymani, Mahdie (Supervisor)
Abstract
In machine learning, classification is of great importance. Unlike the traditional single-label classification in which one instance can have only one label, in multi-label classification tasks, an instance can be associated with a set of labels. Multi-label classifiers have to address many problems including: considering correlations between labels, handling large-scale datasets with many instances and a large set of labels, and having only a fraction of valid label assignments in the training set. To tackle datasets with a large set of labels, recently embedding-based methods have been proposed which seek to represent the label assignments in an intermediate space. Subsequently, given the...
Cataloging briefProbabilistic Approach for Multi-label Classification, M.Sc. Thesis Sharif University of Technology ; Soleymani, Mahdie (Supervisor)
Abstract
In machine learning, classification is of great importance. Unlike the traditional single-label classification in which one instance can have only one label, in multi-label classification tasks, an instance can be associated with a set of labels. Multi-label classifiers have to address many problems including: considering correlations between labels, handling large-scale datasets with many instances and a large set of labels, and having only a fraction of valid label assignments in the training set. To tackle datasets with a large set of labels, recently embedding-based methods have been proposed which seek to represent the label assignments in an intermediate space. Subsequently, given the...
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