The technology of support vector machines (SVM) is being widely used in many research fields at present, but standard SVM does not provide posterior probability that is needed in many uncertain classification problems. To solve this problem, a probability SVM model is built firstly, then the cross entropy and relative cross entropy model for classification problems are built. Finally, the metho...

Quantitative and qualitative description of particles is one of the most important tasks in the Electron Microscopy (EM) analysis. In this paper, we present an algorithm for identifying ball-like nanostructures of gahnite in the Transmission Electron Microscopy (TEM) images. Our solution is based on the cross-entropy clustering which allows to count and measure disk-like objects which are not n...

Abstract. In this paper we present a probabilistic algorithm which factorizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of factors in this model will have their cross entropy maximized or minimized. These priors allow us to construct factorization algorithms that result in maximally statistically different factors, something that generic non-...

In this article, we explore the concept of minimization of information loss (MIL) as a a target for neural network learning. We relate MIL to supervised and unsupervised learning procedures such as the Bayesian maximum a-posteriori (MAP) discriminator, minimization of distortion measures such as mean squared error (MSE) and cross-entropy (CE), and principal component analysis (PCA). To deal wit...

We report on detailed low-temperature measurements of the magnetization, the specific heat and the electrical resistivity on high-quality CeRuSn single crystals. The compound orders antiferromagnetically at T(N) = 2.8 K with the Ce(3+) ions locked within the a-c plane of the monoclinic structure. Magnetization shows that below T(N) CeRuSn undergoes a metamagnetic transition when applying a magn...

In this work, we analyze the cross-entropy function, widely used in classifiers both as a performance measure and as an optimization objective. We contextualize cross-entropy in the light of Bayesian decision theory, the formal probabilistic framework for making decisions, and we thoroughly analyze its motivation, meaning and interpretation from an information-theoretical point of view. In this...

BACKGROUND We compared two spectral entropies, state entropy (SE) and response entropy (RE), based on the irregularity of the EEG, to measure loss of response to verbal command (LOR(verbal)) and noxious stimulus (LOR(noxious)) with the bispectral index (BIS) during propofol infusion with and without remifentanil. METHODS Three groups of 20 patients received an effect-site controlled propofol ...

In this paper, we investigated the problem of discovering periodicity of a certain event in a binary data series and a new method basing on cross entropy is proposed. First, a series of rational partition methods for binary data series are introduced, which can divide the data series into different segments (partition). Then, we use cross entropy to calculate the partition periodicity, which co...