On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas

Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In this paper a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide guidelines for using such an ensemble, and show state-of-the-art performances on a range of multiple instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning Systems, Special Issue on Learning in Non-(geo)metric Space

Refugiados por asociación

Aun cuando no se les haya dado un trato especial a título individual, muchos sirios reúnen los criterios para ser considerados refugiados si nos basamos en que corren peligro de ser perseguidos porque se les presupone asociados, en el sentido más amplio de la palabra, a alguna de las partes del conflicto

Urban Contacts: Orientalist Urban Planning and Le Corbusier in French Colonial Algiers

Algiers, the first French colony in Africa, was conquered in 1830 and gained independence in 1962. During this period, Algiers was constructed into an Orientalist acting ground that was shaped through political, social, economic formations in the built environment. The French colonial fascination with Algiers centered around the casbah, and thus the casbah became a laboratory for ethnographic and urban reflections. The French process of urban planning included military intervention, preservation motivated by exoticism and museology, and superstructure master plans dictated by the present benefit of indigenous communities to the colonial regime. Le Corbusier’s contact with Algiers further expresses the imperialist dynamics and the ways in which myths are physically acted upon. The formation of culture is a main component of the process of imperialism and, according to Edward Said, “by continuing consolidation within education, literature, and the visual and musical arts.” Architecture cannot 1 be removed from this equation, and rather must be understood as a main tool of colonial powers to control experience of culture and maintain Orientalist ideologies. Colonial urban planning, enacted through the politics of preservation and especially present in Le Corbusier’s master plan, is embedded with racial, gendered, and religious colonial frameworks that contribute to and further the French regime in explicit and implicit ways