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

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

Terrain classification by cluster analisys

The digital terrain modelling can be obtained by different methods belonging to two principal categories: deterministic methods (e.g. polinomial and spline functions interpolation, Fourier spectra) and stochastic methods (e.g. least squares collocation and fractals, i.e. the concept of selfsimilarity in probability). To reach good resul ts, both the fi rst and the second methods need same initial suitable information which can be gained by a preprocessing of data named terrain classification. In fact, the deterministic methods require to know how is the roughness of the terrain, related to the density of the data (elevations, deformations, etc.) used for the i nterpo 1 at ion, and the stochast i c methods ask for the knowledge of the autocorrelation function of the data. Moreover, may be useful or very necessary to sp 1 it up the area under consideration in subareas homogeneous according to some parameters, because of different kinds of reasons (too much large initial set of data, so that they can't be processed togheter; very important discontinuities or singularities; etc.). Last but not least, may be remarkable to test the type of distribution (normal or non-normal) of the subsets obtained by the preceding selection, because the statistical properties of the normal distribution are very important (e.g., least squares linear estimations are the same of maximum likelihood and minimum variance ones)

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

Classification of Several Skin Cancer Types Based on Autofluorescence Intensity of Visible Light to Near Infrared Ratio

Skin cancer is a Malignant growth on the skin caused by many factors. The most common skin cancers are Basal Cell Cancer (BCC) and Squamous Cell Cancer (SCC). This research uses a discriminant analysis to classify some tissues of skin cancer based on criterion number of independent variables. An independent variable is variation of excitation light sources (LED lamp), filters, and sensors to measure autofluorescence intensity (IAF) of visible light to near infrared (VIS/NIR) ratio of paraffin embedded tissue biopsy from BCC, SCC, and Lipoma. From the result of discriminant analysis, it is known that the discriminant function is determined by 4 (four) independent variables i.e., blue LED-red filter, blue LED-yellow filter, UV LED-blue filter, and UV LED-yellow filter. The accuracy of discriminant in classifying the analysis of three skin cancer tissues is 100%