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Multi-label classification using error correcting output codes

Tomasz Kajdanowicz, Przemysław Kazienko (2012)

International Journal of Applied Mathematics and Computer Science

A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification...

Multilevel correction adaptive finite element method for semilinear elliptic equation

Qun Lin, Hehu Xie, Fei Xu (2015)

Applications of Mathematics

A type of adaptive finite element method is presented for semilinear elliptic problems based on multilevel correction scheme. The main idea of the method is to transform the semilinear elliptic equation into a sequence of linearized boundary value problems on the adaptive partitions and some semilinear elliptic problems on very low dimensional finite element spaces. Hence, solving the semilinear elliptic problem can reach almost the same efficiency as the adaptive method for the associated boundary...

Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

Hoai Linh Tran, Van Nam Pham, Hoang Nam Vuong (2014)

International Journal of Applied Mathematics and Computer Science

The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak...

Multiplicative Cauchy functional equation and the equation of ratios on the Lorentz cone

Jacek Wesołowski (2007)

Studia Mathematica

It is proved that the solution of the multiplicative Cauchy functional equation on the Lorentz cone of dimension greater than two is a power function of the determinant. The equation is solved in full generality, i.e. no smoothness assumptions on the unknown function are imposed. Also the functional equation of ratios, of a similar nature, is solved in full generality.

Multistage multivariate nested distance: An empirical analysis

Sebastiano Vitali (2018)

Kybernetika

Multistage stochastic optimization requires the definition and the generation of a discrete stochastic tree that represents the evolution of the uncertain parameters in time and space. The dimension of the tree is the result of a trade-off between the adaptability to the original probability distribution and the computational tractability. Moreover, the discrete approximation of a continuous random variable is not unique. The concept of the best discrete approximation has been widely explored and...

Multistage regression model

Lubomír Kubáček (1986)

Aplikace matematiky

Necessary and sufficient conditions are given under which the best linear unbiased estimator (BLUE) β ^ i ( Y 1 , ⋯ , Y i ) is identical with the BLUE β ^ i ( β ^ 1 , ⋯ , β ^ i - 1 , Y i ) ; Y 1 ⋯ , Y i are subvectors of the random vector Y in a general regression model ( Y , X β , ∑ ) , ( β 1 ' , ⋯ , β i ' ) ' = β a vector of unknown parameters; the design matrix X having a special so called multistage struture and the covariance matrix ∑ are given.

Multi-variate correlation and mixtures of product measures

Tim Austin (2020)

Kybernetika

Total correlation (‘TC’) and dual total correlation (‘DTC’) are two classical ways to quantify the correlation among an n -tuple of random variables. They both reduce to mutual information when n = 2 . The first part of this paper sets up the theory of TC and DTC for general random variables, not necessarily finite-valued. This generality has not been exposed in the literature before. The second part considers the structural implications when a joint distribution μ has small TC or DTC. If TC ( μ ) = o ( n ) , then μ is...

Multivariate extensions of expectiles risk measures

Véronique Maume-Deschamps, Didier Rullière, Khalil Said (2017)

Dependence Modeling

This paper is devoted to the introduction and study of a new family of multivariate elicitable risk measures. We call the obtained vector-valued measures multivariate expectiles. We present the different approaches used to construct our measures. We discuss the coherence properties of these multivariate expectiles. Furthermore, we propose a stochastic approximation tool of these risk measures.

Multivariate Extreme Value Theory - A Tutorial with Applications to Hydrology and Meteorology

Anne Dutfoy, Sylvie Parey, Nicolas Roche (2014)

Dependence Modeling

In this paper, we provide a tutorial on multivariate extreme value methods which allows to estimate the risk associated with rare events occurring jointly. We draw particular attention to issues related to extremal dependence and we insist on the asymptotic independence feature. We apply the multivariate extreme value theory on two data sets related to hydrology and meteorology: first, the joint flooding of two rivers, which puts at risk the facilities lying downstream the confluence; then the joint...

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