Xari Rovira
ESADE, Operations, Innovation and Data Sciences, Faculty Member
This paper lies within the domain of supervised learning algorithms based on neural networks whose architecture corresponds to radial basis functions. A methodology to use RBF when the descriptors of the patterns are given by means of... more
This paper lies within the domain of supervised learning algorithms based on neural networks whose architecture corresponds to radial basis functions. A methodology to use RBF when the descriptors of the patterns are given by means of their orders of magnitude is ...
Research Interests:
Research Interests:
ABSTRACT Performance measurement is a key issue when a company is designing new strategies to improve resource allocation. This paper offers a new methodology inspired by classic importance-performance analysis (IPA) that provides a... more
ABSTRACT Performance measurement is a key issue when a company is designing new strategies to improve resource allocation. This paper offers a new methodology inspired by classic importance-performance analysis (IPA) that provides a global index of importance versus performance for firms. This index compares two rankings of the same set of features regarding importance and performance, taking into account underperforming features. The marginal contribution of each feature to the proposed global index defines a set of iso-curves that represents an improvement in the IPA diagram. The defined index, together with the new version of the diagram, will enable the assessment of a firm’s overall performance and, therefore, enhance decision making in the allocation of resources. The proposed methodology has been applied to a Taiwanese multi-format retailer and managerial perceptions of performance and importance are compared to assess the firm’s overall performance.
Research Interests:
The construction of machines able to learn to classify patterns, that is to assign them a class among a finite set of possibilities, is one of the main goals of Artificial Intelligence. Lately, different learning machines based on... more
The construction of machines able to learn to classify patterns, that is to assign them a class among a finite set of possibilities, is one of the main goals of Artificial Intelligence. Lately, different learning machines based on kernels, such as Support Vector Machines (SVM), have been ...
Research Interests:
Research Interests:
Research Interests:
This paper presents the concepts underlying the design of an intelligent system based on the absolute orders of magnitude model to define qualitative operators able to dea l with a set of variables defined in different reference scale s... more
This paper presents the concepts underlying the design of an intelligent system based on the absolute orders of magnitude model to define qualitative operators able to dea l with a set of variables defined in different reference scale s and with different influence ...
Research Interests:
This paper presents a method for evaluating qualitative orders of magnitude information in multi-attribute decision-making. It allows the selection of an alternative from among a set of alternatives. These are characterized by having all... more
This paper presents a method for evaluating qualitative orders of magnitude information in multi-attribute decision-making. It allows the selection of an alternative from among a set of alternatives. These are characterized by having all descrip-tors defined in orders of ...
Research Interests:
This paper lies within the domain of learning algorithms based on kernels of Support Vector Machines. A kernel is constructed over the discrete structure of absolute orders of magnitude spaces. This kernel is based on an explicit... more
This paper lies within the domain of learning algorithms based on kernels of Support Vector Machines. A kernel is constructed over the discrete structure of absolute orders of magnitude spaces. This kernel is based on an explicit function, defined from the space of k-tuples of ...
Research Interests:
This paper lies within the domain of supervised learning algorithms based on neural networks whose architecture corresponds to radial basis functions. A methodology to use RBF when the descriptors of the patterns are given by means of... more
This paper lies within the domain of supervised learning algorithms based on neural networks whose architecture corresponds to radial basis functions. A methodology to use RBF when the descriptors of the patterns are given by means of their orders of magnitude is ...
Research Interests:
Resumen Los algoritmos de aprendizaje basados en Funciones Núcleo, particularmente las Máquinas de Soporte Vectorial (MSV), han proporcionado buenos resultados en problemas de clasificación con patrones de entrada no separables... more
Resumen Los algoritmos de aprendizaje basados en Funciones Núcleo, particularmente las Máquinas de Soporte Vectorial (MSV), han proporcionado buenos resultados en problemas de clasificación con patrones de entrada no separables linealmente. El uso de las Funciones Núcleo permite aplicar estos algoritmos de inferencia incluso sobre información proveniente de un conjunto sin estructura de espacio euclideo. Al considerar una Función Núcleo, los datos se proyectan de forma implícita sobre un nuevo espacio de ...