34 research outputs found
3C-Silicon Carbide (SiC) as a material of choice for high-performance low-cost power electronic devices
No full textSubclass error correcting output codes using fisher's linear discriminant ratio
No full textError-Correcting Output Codes (ECOC) with subclasses reveal a common way to solve multi-class classification problems. According to this approach, a multiclass problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic mutual information (FQMI) procedure. However, FQMI is not applicable on large datasets due to its high algorithmic complexity. In this paper we propose Fisher's Linear Discriminant Ratio (FLDR) as an alternative decomposition criterion which is of much less computational complexity and achieves in most experiments conducted better classification performance. Furthermore, we compare FLDR against FQMI for facial expression recognition over the Cohn-Kanade database. © 2010 IEEE.ISI
Mutual information measures for subclass error-correcting output codes classification
No full textError-Correcting Output Codes (ECOCs) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclasses technique (sub-ECOC), where by splitting the initial classes of the problem we aim to the creation of larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a searching procedure known as sequential forward floating search (SFFS). The SFFS algorithm in each step searches for the optimum binary separation of the classes that compose the multi-class problem. The separation decision is based on the maximization or minimization of a criterion function. The standard criterion used is the maximization of the mutual information (MI) between the bi-partitions created in each step of the SFFS. The materialization of the MI measure is achieved by a method called fast quadratic Mutual Information (FQMI). Although FQMI is quite accurate in modelling the MI, its computation is of high algorithmic complexity, which as a consequence makes the ECOC and sub-ECOC techniques applicable only on small datasets. In this paper we present some alternative separation criteria of reduced computational complexity that can be used in the SFFS algorithm. Furthermore, we compare the performance of these criteria over several multi-class classification problems. © Springer-Verlag Berlin Heidelberg 2010.ISI
Optimizing subclass discriminant error correcting output codes using particle swarm optimization
No full textError-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem we create larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a discriminant tree creation procedure. This discriminant tree's creation is controlled by a triplet of thresholds that define a set of user defined splitting standards. The selection of the thresholds plays a major role in the classification performance. In our work we show that by optimizing these thresholds via particle swarm optimization we improve significantly the classification performance. Moreover, using Support Vector Machines (SVMs) as classifiers we can optimize in the same time both the thresholds of sub-ECOC and the parameters C and φ of the SVMs, resulting in even better classification performance. Extensive experiments in both real and artificial data illustrate the superiority of the proposed approach in terms of performance. © 2010 IEEE.ISI
An experimentally driven assessment of the dynamic-on resistance in correlation to other performance indicators in commercial Gallium Nitride power devices
No full textThis work provides an experimentally driven performance comparison of commercial Gallium Nitride on Silicon (GaN-on-Si) power devices rated 600-650V at room and elevated temperatures with the focus being in assessing the on resistance (RON) increase due to hard switching in correlation to other performance indicators. Device technologies evaluated include the Enhancement (E-mode) AlGaN/GaN Hybrid Drain p-GaN layer Gate Injection Transistor (p-GaN HD-GIT), the cascode AlGaN/GaN High Electron Mobility Transistor (cascode HEMT). For the dynamic RON analysis, a special setup was utilized which allows synchronized drain and gate pulses, and the ability to switch from OFF to ON in as little as 20μs. The ability to apply a wide range of voltage levels, stress duration and temperature enabled measurable increase in the dynamic RON in both the cascode HEMT and the p-GaN HD-GIT. Nonetheless, the results highlight a strong difference in their robustness. </p
The political economy of regime change: A case study of Greece
No full textThe subject of this dissertation is the process of change from military to civilian rule in Greece in 1974. My concerns are predominantly conceptual and analytic rather than descriptive. Three civil-military configurations of rule are identified in praetorian societies: a civil-military configuration, whereby both civilian and military actors have autonomous input in politics and where no relation of subordination exists between them; a civil-military configuration of military hegemony and civilian subordination; and a civil-military configuration of civilian hegemony and military subordination. My main concern lies with the analysis of the process of change from military to civilian hegemony. My principal assumption is that regime transitions are marked by uncertainty and structural fluidity. Structural variables define the parameters but do not determine the process or the outcome of regime change. Unlike revolutions, transitions are changes from above that bring about significant political changes but little, if any, immediate socioeconomic change. I conceptualize the process of transition from military to civilian hegemony as a set of strategic games among rational civilian and military actors promoting different transitional strategies. This study's aim is threefold: to explain regime transition in Greece; to suggest generalizations for transitions in other praetorian societies; and to apply game theory to civil-military relations.Source: Dissertation Abstracts International, Volume: 51-08, Section: A, page: 2857.Advisors: Louis W. Goodman.Ph.D. American University 1989.Englis
Optimizing linear discriminant error correcting output codes using particle swarm optimization
No full textError Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multi-class problem. In this paper we consider the optimization of the Linear Discriminant Error Correcting Output Codes framework using Particle Swarm Optimization. In particular, we apply the Particle Swarm Optimization algorithm in order to optimally select the free parameters that control the split of the initial problem's classes into sub-classes. Moreover, by using the Support Vector Machine as classifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance. © 2011 Springer-Verlag.ISI
Laplacian Support Vector Analysis for Subspace Discriminative Learning
No full textIn this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which capture the discriminant information that lies in the subspace orthogonal to the standard Laplacian SVMs. We show that the optimal vectors on these deflated subspaces can be computed by successively training a standard SVM with specially designed deflation kernels. The resulting normal vectors contain discriminative information that can be used for feature extraction. In our analysis, we derive an explicit form for the deflation matrix of the mapped features in both the initial and the Hilbert space by using the kernel trick and thus, we can handle linear and non-linear deflation transformations. Experimental results in several benchmark datasets illustrate the strength of our proposed algorithm.IVR
Graph Embedded Nonparametric Mutual Information For Supervised Dimensionality Reduction
No full textIn this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their cor- responding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Further- more, recent quadratic nonparametric implementations of MI are computationally efficient and do not require any prior assumptions about the class densities. We show that the quadratic nonparametric MI can be formulated as a kernel objective in the graph embedding framework. Moreover, we propose its linear equivalent as a novel linear dimensionality reduction algorithm. The derived methods are compared against the state-of-the-art dimensionality reduction algorithms with various classifiers and on various benchmark and real-life datasets. The experimental results show that nonparametric MI as an optimization objective for dimensionality reduction gives comparable and in most of the cases better results compared with other dimensionality reduction methods.IVR
