1,721,435 research outputs found
Traumatic corneal flap displacement after laser in situ keratomileusis (LASIK) [Erratum]
No full textTsai TH, Peng KL, Lin CJ. International Medical Case Reports Journal. 2017;10:143–148On page 144, right column, last paragraph, “Informed consent was obtained from the patient for publication of this case report and accompanying images. The ethics committee of ....... did not require written informed consent be obtained from the patient because .....” should have read “Written informed consent was obtained from the patient for publication of this Case Report and accompanying images”. Read the original articl
Tracheid length and microfibril angle of young Taiwania grown under different thinning and pruning treatments.
No full textEnhancement of glucose transporter expression of brain endothelial cells by vascular endothelial growth factor derived from glioma exposed hypoxia
No full textFlexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters
Full text linkThere are two notoriously hard problems in cluster analysis, estimating the number of clusters, and checking whether the population to be clustered is not actually homogeneous. Given a dataset, a clustering method and a cluster validation index, this paper proposes to set up null models that capture structural features of the data that cannot be interpreted as indicating clustering. Artificial datasets are sampled from the null model with parameters estimated from the original dataset. This can be used for testing the null hypothesis of a homogeneous population against a clustering alternative. It can also be used to calibrate the validation index for estimating the number of clusters, by taking into account the expected distribution of the index under the null model for any given number of clusters. The approach is illustrated by three examples, involving various different clustering techniques (partitioning around medoids, hierarchical methods, a Gaussian mixture model), validation indexes (average silhouette width, prediction strength and BIC), and issues such as mixed-type data, temporal and spatial autocorrelation
Support Vector Machines in R
Full text linkBeing among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematic
Support Vector Machines in R
Full text linkBeing among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.
Tissue distribution of arsenic species in rabbits after single and multiple parenteral administration of arsenic trioxide: tissue accumulation and the reversibility after washout are tissue-selective.
No full textDifferentiation of ketamine effects on renal nerve activity and renal blood flow in rats
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A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
Full text linkIn this work we present a novel way to solve the sub-problems that originate when using decomposition algorithms to train Support Vector Machines (SVMs). State-of-the-art Sequential Minimization Optimization (SMO) solvers reduce the original problem to a sequence of sub-problems of two variables for which the solution is analytical. Although considering more than two variables at a time usually results in a lower number of iterations needed to train an SVM model, solving the sub-problem becomes much harder and the overall computational gains are limited, if any. We propose to apply the two-variables decomposition method to solve the sub-problems themselves and experimentally show that it is a viable and efficient way to deal with sub-problems of up to 50 variables. As a second contribution we explore different ways to select the working set and its size, combining first-order and second-order working set selection rules together with a strategy for exploiting cached elements of the Hessian matrix. An extensive numerical comparison shows that the method performs considerably better than state-of-the-art software
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