Voting Operators in the Space of Choice Functions

Assuming the individual and collective opinions are given as choice functions, a new formalization of the voting problem is considered. The notions of a local functional operator and the closedness of domains in choice-functional space relative to local operators are introduced. The problem of voting is reduced to an analysis of three kinds of operator classes and their mutual relations. The functional analogues well known in the theory of Arrow's paradox results are established

Lexicographic choice functions without archimedeanicity

We investigate the connection between choice functions and lexicographic probabilities, by means of the convexity axiom considered by Seidenfeld, Schervisch and Kadane (2010) but without imposing any Archimedean condition. We show that lexicographic probabilities are related to a particular type of sets of desirable gambles, and investigate the properties of the coherent choice function this induces via maximality. Finally, we show that the convexity axiom is necessary but not sufficient for a coherent choice function to be the infimum of a class of lexicographic ones

Voting Operators in the Space of Choice Functions

Assuming the individual and collective opinions are given as choice functions, a new formalization of the voting problem is considered. The notions of a local functional operator and the closedness of domains in choice-functional space relative to local operators are introduced. The problem of voting is reduced to an analysis of three kinds of operator classes and their mutual relations. The functional analogues well known in the theory of Arrow's paradox results are established

Theory, Politics... and History? Early post-war Soviet Control Engineering

A fascinating feature of post-war control engineering in the former Soviet Union was the rôle played by the study of the history of the discipline. Even before and during World War II some Soviet control scientists were actively researching the history of their subject; while after the war, historical studies played an important part both in technical developments and in legitimating a native Russian tradition. Two of the most important figures in this historical activity were A. A. Andronov and I. N. Voznesenskii, whose contributions are briefly considered

Notes About Theory of Pseudo-Criteria and Binary Pseudo-Relations and Their Application to the Theory of Choice and Voting

The pages of this working paper are copies of transparencies used in a lecture on the general theory of choice given at the California Institute of Technology June 1991

Notes About Theory of Pseudo-Criteria and Binary Pseudo-Relations and Their Application to the Theory of Choice and Voting

The pages of this working paper are copies of transparencies used in a lecture on the general theory of choice given at the California Institute of Technology June 1991

String pattern recognition using evolving spiking neural networks and quantum inspired particle swarm optimization

This paper proposes a novel method for string pattern recognition using an Evolving Spiking Neural Network (ESNN) with Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals an interesting concept of QiPSO by representing information as binary structures. The mechanism optimizes the ESNN parameters and relevant features using the wrapper approach simultaneously. The N-gram kernel is used to map Reuters string datasets into high dimensional feature matrix which acts as an input to the proposed method. The results show promising string classification results as well as satisfactory QiPSO performance in obtaining the best combination of ESNN parameters and in identifying the most relevant features

Automatic detection of limb prominences in 304 A EUV images

A new algorithm for automatic detection of prominences on the solar limb in 304 A EUV images is presented, and results of its application to SOHO/EIT data discussed. The detection is based on the method of moments combined with a classifier analysis aimed at discriminating between limb prominences, active regions, and the quiet corona. This classifier analysis is based on a Support Vector Machine (SVM). Using a set of 12 moments of the radial intensity profiles, the algorithm performs well in discriminating between the above three categories of limb structures, with a misclassification rate of 7%. Pixels detected as belonging to a prominence are then used as starting point to reconstruct the whole prominence by morphological image processing techniques. It is planned that a catalogue of limb prominences identified in SOHO and STEREO data using this method will be made publicly available to the scientific community

Profiles and Majority Voting-Based Ensemble Method for Protein Secondary Structure Prediction

Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted attention recently. Today, the main goal remains to improve the prediction quality of the secondary structure elements. The prediction accuracy has been continuously improved over the years, especially by using hybrid or ensemble methods and incorporating evolutionary information in the form of profiles extracted from alignments of multiple homologous sequences. In this paper, we investigate how best to combine k-NNs, ANNs and Multi-class SVMs (M-SVMs) to improve secondary structure prediction of globular proteins. An ensemble method which combines the outputs of two feed-forward ANNs, k-NN and three M-SVM classifiers has been applied. Ensemble members are combined using two variants of majority voting rule. An heuristic based filter has also been applied to refine the prediction. To investigate how much improvement the general ensemble method can give rather than the individual classifiers that make up the ensemble, we have experimented with the proposed system on the two widely used benchmark datasets RS126 and CB513 using cross-validation tests by including PSI-BLAST position-specific scoring matrix (PSSM) profiles as inputs. The experimental results reveal that the proposed system yields significant performance gains when compared with the best individual classifier