نتایج جستجو برای: support vector machine glioblastoma multiforme magneticresonance spectroscopy

تعداد نتایج: 1218563  

2011
Sascha Klemenjak Björn Waske

To segment a image with strongly varying object sizes results generally in under-segmentation of small structures or over-segmentation of big ones, which consequences poor classification accuracies. A strategy to produce multiple segmentations of one image and classification with support vector machines (SVM) of this segmentation stack afterwards is shown.

Journal: :JBNC - JORNAL BRASILEIRO DE NEUROCIRURGIA 2018

2001
Grace Wahba Yi Lin Yoonkyung Lee Hao Zhang

We rederive a form of Joachims’ ξα method for tuning Support Vector Machines by the same approach as was used to derive the GACV, and show how the two methods are related. We generalize the ξα method to the nonstandard case of nonrepresentative training set and unequal misclassification costs and compare the result to the GACV estimate for the standard and nonstandard cases.

2009
Daniel Pasailă

Using Support Vector Machines for MiRNA Identification

Journal: :Tumori 2008
Silvia Scoccianti Beatrice Detti Icro Meattini Alberto Iannalfi Angela Sardaro Barbara Grilli Leonulli Francesco Martinelli Lorenzo Bordi Gianni Pellicanò Giampaolo Biti

BACKGROUND Glioblastoma multiforme infrequently metastasizes to the leptomeninges and even more rarely to the spinal cord. Moreover, very few patients with intracranial glioblastoma develop symptoms from spinal dissemination, with most patients not surviving long enough for spinal disease to become clinically evident. CASE REPORT We present a rare case of symptomatic diffuse spinal leptomenin...

Journal: :Journal of Computer Science and Cybernetics 2021

In binary classification problems, two classes of data seem to be different from each other. It is expected more complicated due the clusters in class also tend different. Traditional algorithms as Support Vector Machine (SVM) or Twin (TWSVM) cannot sufficiently exploit structural information with cluster granularity data, cause limitation on capability simulation trends. Structural (S-TWSVM) e...

Heidari, M.,

Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on wavelet packet transform (WPT) and multiple classifier fusion. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the...

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