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Keywords: Multi-label ranking Multi-label classification Comparison of multi-label learning methods a b s t r a c t Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In this paper, we present an extensive experimental(More)
Gait is a persons manner of walking. It is a biometric that can be used for identifying humans. Gait is an unobtrusive metric that can be obtained from distance, and this is its main strength compared to other biometrics. In this paper we construct and evaluate feature sets with the purpose of finding out the role of different types of features and body(More)
A common approach to solving multi-label learning problems is to use problem transformation methods and dichotomizing classifiers as in the pair-wise decomposition strategy. One of the problems with this strategy is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning(More)
Multi-label classification (MLC) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLC are the large-scale problem and the high dimensionality of the label space, which have a strong(More)
Multi-label learning (MLL) problems abound in many areas, including text categoriza-tion, protein function classification, and semantic annotation of multimedia. An issues that severely limits the applicability of many current machine learning approaches to MLL are the large-scale problem, which have a strong impact on the computational complexity of(More)
A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing clas-sifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in(More)
In this paper, we describe an approach to the automatic plant identification task of the LifeCLEF 2014 lab. The image descrip-tors for all submitted runs were obtained using the bag-of-visual-words method. For the leaf scans, we use multiscale triangular shape descriptor and for the other plant organs Opponent SIFT extracted around points of interest(More)