Privatization and policy competition for FDI

In this paper, we provide an explanation of why privatization may attract foreign investors interested in entering a regional market. Privatization turns the formerly-public firm into a less aggressive competitor since profit- maximizing output is lower than the welfare-maximizing one. The drawback is that social welfare generally decreases. We also investigate tax/subsidy competition for FDI before and after privatization. We show that policy competition is irrelevant in the presence of a public firm serving just its domestic market. By contrast, following privatization, it endows the big country with an instrument which can be used either to reduce the negative impact on welfare of an FDI-attracting privatization or to protect the domestic industry from foreign competitors

Classification

In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class

Effective Classification using a small Training Set based on Discretization and Statistical Analysis

This work deals with the problem of producing a fast and accurate data classification, learning it from a possibly small set of records that are already classified. The proposed approach is based on the framework of the so-called Logical Analysis of Data (LAD), but enriched with information obtained from statistical considerations on the data. A number of discrete optimization problems are solved in the different steps of the procedure, but their computational demand can be controlled. The accuracy of the proposed approach is compared to that of the standard LAD algorithm, of Support Vector Machines and of Label Propagation algorithm on publicly available datasets of the UCI repository. Encouraging results are obtained and discusse

Leaf Classification

The purpose of this resource is to develop a classification system for a set of objects and learn about hierarchical classification systems. Any set of objects, such as insects or rocks, may be used as well. Educational levels: Primary elementary, Intermediate elementary, Middle school, High school

Acoustic Scene Classification

This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship (EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council

Methodology of the biological risk classification of animal pathogens in Belgium

The biological hazards posed by micro-organisms have lead to their categorisation into risk groups and the elaboration of classification lists. Current classification systems rely on criteria defined by the World Health Organization, which cover the severity of the disease the micro-organism might cause, its ability to spread and the availability of prophylaxis or efficient treatment. Animal pathogens are classified according to the definitions of the World Organization of Animal Health, which also consider economic aspects of disease. In Europe, classification is often directly linked to containment measures. The Belgian classification system however, only considers the inherent characteristics of the micro-organism, not its use, making the risk classification independent of containment measures. A common classification list for human and animal pathogens has been developed in Belgium using as comprehensive an approach as possible. Evolution of scientific knowledge will demand regular updating of classification lists. This paper describes the Belgian risk classification system and the methodology that was used for its peer-reviewed revision (with a focus on animal pathogens)

Compressive Classification

This paper derives fundamental limits associated with compressive classification of Gaussian mixture source models. In particular, we offer an asymptotic characterization of the behavior of the (upper bound to the) misclassification probability associated with the optimal Maximum-A-Posteriori (MAP) classifier that depends on quantities that are dual to the concepts of diversity gain and coding gain in multi-antenna communications. The diversity, which is shown to determine the rate at which the probability of misclassification decays in the low noise regime, is shown to depend on the geometry of the source, the geometry of the measurement system and their interplay. The measurement gain, which represents the counterpart of the coding gain, is also shown to depend on geometrical quantities. It is argued that the diversity order and the measurement gain also offer an optimization criterion to perform dictionary learning for compressive classification applications.Comment: 5 pages, 3 figures, submitted to the 2013 IEEE International Symposium on Information Theory (ISIT 2013