本研究由「公平性」著眼，分析國內教育資源在各縣市的分配情形，主要 目的為：ぇ探討教育對經濟成長、區域發展的影響，及與個人所得、福利 分配的關係；え研究教育機會公平的真諦與衡量的方法；ぉ檢視過去四十 年來教育資源分配的縱貫演變情形與意義，以了解教育政策公平性的實質 效果與缺失，找尋未來政策的發展方向。研究對象為臺灣地區二十三縣市 ，資料來源為各縣市的教育、財政與人口統計資料，依水平公平、垂直公 平與財政中性原則三向度分別衡量各縣市納稅人與學生的待遇公平性。研 究方法以吉尼係數與相關係數分別衡量公平性，以迴歸分析探索教育經費 的影響因素，並以 Shorrocks 流動性測度檢視資源分配的變動，以集群 分析描述目前教育資源分配的不均情形。主要研究結果為：ぇ納稅人受益 公平：高等教育與高中的分布最為不均，都會區資源增加尤速；高職分布 較為均衡；國民教育資源則在人口比與空間分布上都相當均等。え學生受 益公平：單位學生教育經費分配允稱公平，但每校支出不均。迴歸分析發 現各縣市教育經費受人事費、縣市財富、學生數與學校數影響，偏遠地區 生師比低，單位學生教育經費高，但每校教學與設備支出較少。顯示縣市 教育經費支出以單位學生為基本考量，卻忽略學校規模大小的影響。ぉ納 稅人負擔公平：部份所得較低縣市的稅收與教育捐負擔高於台北、桃園、 台中、台南、高雄等縣及基隆市，不符水平及垂直公平。お垂直公平：省 與中央的補助符合濟弱扶貧的精神，但以補足財政缺口為主，缺乏刺激效 果，且補助收入佔比例過高，形成過度依賴，有損地方自主精神，相對也 使公共財的分配失卻效率。か集群分析結果：都會區（省、直轄市與較富 有縣份）在選擇性教育階段佔優勢，未來應行調整。最後根據研究結果提 出因應措施，並對未來研究方法提出建議。

Nous proposons dans cette thèse la méthode STREE de construction d'arbres de décision avec des données symboliques. Ce type de données permet de caractériser des individus de niveau supérieur qui peuvent être des classes ou catégories d’individus ou des concepts au sens des treillis de Galois. Les valeurs des variables, appelées variables symboliques, peuvent être des ensembles, des intervalles ou des histogrammes. Le critère de partitionnement récursif est une combinaison d'un critère par rapport aux variables explicatives et d'un critère par rapport à la variable à expliquer. Le premier critère est la variation de la variance des variables explicatives. Quand il est appliqué seul, STREE correspond à une méthode descendante de classification non supervisée. Le second critère permet de construire un arbre de décision. Il s'agit de la variation de l'indice de Gini si la variable à expliquer est nominale et de la variation de la variance si la variable à expliquer est continue ou bien est une variable symbolique. Les données classiques sont un cas particulier de données symboliques sur lesquelles STREE peut aussi obtenir de bons résultats. Il en ressort de bonnes performances sur plusieurs jeux de données UCI par rapport à des méthodes classiques de Data Mining telles que CART, C4.5, Naive Bayes, KNN, MLP et SVM. STREE permet également la construction d'ensembles d'arbres de décision symboliques soit par bagging soit par boosting. L'utilisation de tels ensembles a pour but de pallier les insuffisances liées aux arbres de décisions eux-mêmes et d'obtenir une décision finale qui est en principe plus fiable que celle obtenue à partir d'un arbre unique. / In this thesis, we propose the STREE methodology for the construction of decision trees with symbolic data. This data type allows us to characterize individuals of higher levels which may be classes or categories of individuals or concepts within the meaning of the Galois lattice. The values of the variables, called symbolic variables, may be sets, intervals or histograms. The criterion of recursive partitioning is a combination of a criterion related to the explanatory variables and a criterion related to the dependant variable. The first criterion is the variation of the variance of the explanatory variables. When it is applied alone, STREE acts as a top-down clustering methodology. The second criterion enables us to build a decision tree. This criteron is expressed as the variation of the Gini index if the dependant variable is nominal, and as the variation of the variance if thedependant variable is continuous or is a symbolic variable. Conventional data are a special case of symbolic data on which STREE can also get good results. It has performed well on multiple sets of UCI data compared to conventional methodologies of Data Mining such as CART, C4.5, Naive Bayes, KNN, MLP and SVM. The STREE methodology also allows for the construction of ensembles of symbolic decision trees either by bagging or by boosting. The use of such ensembles is designed to overcome shortcomings related to the decisions trees themselves and to obtain a finaldecision that is in principle more reliable than that obtained from a single tree.

The aim of this thesis is to show how the unconventional monetary policy rounds of Quantitative Easing introduced in the United States between 2008 and 2014 have led to an increase in wealth inequality. The need for the thesis arises due to the uncharted nature of QE and because of more and more information is surfacing to light which points to this connection. By analysing the distribution of these funds and adding it to the then base distribution of money supply, this study was able to determine a significant 10 % increase in the Gini Index. Furthermore it highlights how a large portion of wealth was transferred from the middle class over to the top 5 % income households. Starting from a set of assumptions the calculation is performed by extrapolating the data required and by isolating the system from any external variables. The result is a theoretical model meant to describe the mechanism that links Quantitative Easing to wealth inequality. Moreover a detailed comparison is provided with the effect of a conventional monetary policy such as Open-Market Operations. Finally solutions to this issue are being discussed from economical, political and fiscal standpoints.

The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.

A measure of the quality of Voronoi tessellations resulting from various mesh generators founded on feature-driven models is introduced in this work. A planar tessellation covers an image with polygons of various shapes and sizes. Tessellations have potential utility due to their geometry and the opportunity to derive useful information from them for object recognition, image processing and classification. Problem domains including images are generally feature-endowed, non-random domains. Generators modeled otherwise may easily guarantee quality of meshes but certainly bear no reference to features of the meshed problem domain. They are therefore unsuitable in point pattern identification, characterization and subsequently the study of meshed regions. We therefore found generators on features of the problem domain. This provides a basis for element quality studies and improvement based on quality criteria. The resulting polygonal meshes tessellating an n-dimensional digital image into convex regions are of varying element qualities. Given several types of mesh generating sets, a measure of overall solution quality is introduced to determine their effectiveness. Given a tessellation of general and mixed shapes, this presents a challenge in quality improvement. The Centroidal Voronoi Tessellation (CVT) technique is developed for quality improvement and guarantees of mixed, general-shaped elements and to preserve the validity of the tessellations. Mesh quality indicators and entropies introduced are useful for pattern studies, analysis, recognition and assessing information. Computed features of tessellated spaces are explored for image information content assessment and cell processing to expose detail using information theoretic methods. Tessellated spaces also furnish information on pattern structure and organization through their quality distributions. Mathematical and theoretical results obtained from these spaces help in understanding Voronoi diagrams as well as for their successful applications. Voronoi diagrams expose neighbourhood relations between pattern units. Given this realization, the foundation of near sets is developed for further applications. / February 2017