1,721,032 research outputs found
Le risorse interculturali nelle dinamiche urbane contemporane
No full textTITOLO DEL CONGRESSO: "Vecchi territori, nuovi mondi: la geografia nelle emergenze del 2000", a cura di CALAFIORE G., PALAGIANO C., PARATORE E
Sparse ι1 and ι2 center classifiers
No full textThe nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest centroid. Thanks to its very low computational cost, the nearest-centroid classifier is still widely used in machine learning, despite the development of many other more sophisticated classification methods. In this paper, we propose two sparse variants of the nearest-centroid classifier, based respectively on ι1 and ι2 distance criteria. The proposed sparse classifiers perform simultaneous classification and feature selection, by detecting the features that are most relevant for the classification purpose. We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) and at a quasi-linear computational cost. The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection techniques, while having a significantly lower computational cost
Age class structure in SIRD models for the COVID-19 - An analysis of Tennessee data
Full text linkThe COVID-19 pandemic is bringing disruptive effects on the healthcare system, economy and social life of countries all over the world. Even though the elder portion of the population is the most severely affected by the coronavirus disease, the counter-measures introduced so far by the governments do not take into account age structure, and the restrictions act uniformly on the population irrespectively of age. In this paper, we introduce a SIRD model with age classes for studying the impact on the epidemic evolution of lockdown policies applied heterogeneously on the different age groups of the population. The proposed model is then applied to COVID-19 data from the state of Tennessee. The simulation results suggest that a selective lockdown, while having a lighter socioeconomic impact, may bring benefits in terms of reduction of the mortality rate that are comparable to the ones obtained by a uniform lockdown
Sparse l1- and l2-Center Classifiers
No full textIn this article, we discuss two novel sparse versions of the classical nearest-centroid classifier. The proposed sparse classifiers are based on l1 and l2 distance criteria, respectively, and perform simultaneous feature selection and classification, by detecting the features that are most relevant for the classification purpose. We formally prove that the training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) at a linear computational cost. Especially, the proposed sparse classifiers are trained in O(mn)+O(młog k) operations, where n is the number of samples, m is the total number of features, and k łeq m is the number of features to be retained in the classifier. Furthermore, the complexity of testing and classifying a new sample is simply O(k) for both methods. The proposed models can be employed either as stand-alone sparse classifiers or fast feature-selection techniques for prefiltering the features to be later fed to other types of classifiers (e.g., SVMs). The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection and classification techniques while having a substantially lower computational cost
Multiclass Sparse Centroids With Application to Fast Time Series Classification
Full text linkIn this article, we propose an efficient multiclass classification scheme based on sparse centroids classifiers. The proposed strategy exhibits linear complexity with respect to both the number of classes and the cardinality of the feature space. The classifier we introduce is based on binary space partitioning, performed by a decision tree where the assignation law at each node is defined via a sparse centroid classifier. We apply the presented strategy to the time series classification problem, showing by experimental evidence that it achieves performance comparable to that of state-of-the-art methods, but with a significantly lower classification time. The proposed technique can be an effective option in resource-constrained environments where the classification time and the computational cost are critical or, in scenarios, where real-time classification is necessary
Random convex programs for distributed multi-agent consensus
Full text linkWe consider convex optimization problems with N randomly drawn convex constraints. Previous work has shown that the tails of the distribution of the probability that the optimal solution subject to these constraints will violate the next random constraint, can be bounded by a binomial distribution. In this paper we extend these results to the violation probability of convex combinations of optimal solutions of optimization problems with random constraints and different cost objectives. This extension has interesting applications to distributed multi-agent consensus algorithms in which the decision vectors of the agents are subject to random constraints and the agents' goal is to achieve consensus on a common value of the decision vector that satisfies the constraints. We give explicit bounds on the tails of the probability that the agents' decision vectors at an arbitrary iteration of the consensus protocol violate further constraint realizations. In a numerical experiment we apply these results to a model predictive control problem in which the agents aim to achieve consensus on a control sequence subject to random terminal constraints
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