A novel approach to mass detection in digital mammography based on Support Vector Machines(SVM)

2003 ◽  
pp. 399-401 ◽  
Author(s):  
Renato Campanini ◽  
Armando Bazzani ◽  
Alessandro Bevilacqua ◽  
Dante Bollini ◽  
Danilo Dongiovanni ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Digital Mammography ◽  
Support Vector ◽  
Mass Detection ◽  
Novel Approach ◽  
Vector Machines

scholarly journals A novel approach for accurate prediction of spontaneous passage of ureteral stones: Support vector machines

2006 ◽  
Vol 69 (1) ◽  
pp. 157-160 ◽  
Author(s):  
F. Dal Moro ◽  
A. Abate ◽  
G.R.G. Lanckriet ◽  
G. Arandjelovic ◽  
P. Gasparella ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Accurate Prediction ◽  
Ureteral Stones ◽  
Support Vector ◽  
Spontaneous Passage ◽  
Novel Approach ◽  
Vector Machines

Outdoor Navigating Scene Labeling Using Importance Factor Based I-RELIEF and Feature Weighted Support Vector Machines

2014 ◽  
Vol 511-512 ◽  
pp. 467-474
Author(s):  
Jun Tu ◽  
Cheng Liang Liu ◽  
Zhong Hua Miao
Keyword(s):  
Support Vector Machines ◽  
Image Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Terrain Classification ◽  
Relief Algorithm ◽  
Novel Approach ◽  
Scene Labeling ◽  
Vector Machines ◽  
Importance Factor

Feature selection plays an important role in terrain classification for outdoor robot navigation. For terrain classification, the image data usually have a large number of feature dimensions. The better selection of features usually results in higher labeling accuracy. In this work, a novel approach for terrain perception using Importance Factor based I-Relief algorithm and Feature Weighted Support Vector Machines (IFIR-FWSVM) is put forward. Firstly, the weight of each feature for classification is computed by using Importance Factor based I-Relief algorithm (IFIR) and the irrelevant features are eliminated. Then the weighted features are used to compute the kernel functions of SVM and trained the classifier. Finally, the trained SVM is employed to predict the terrain label in the far-field regions. Experimental results based on DARPA datasets show that the proposed method IFIR-FWSVM is superior over traditional SVM.

Handedness tests for preschool children: A novel approach based on graphics tablets and support vector machines

2012 ◽  
Vol 12 (4) ◽  
pp. 1390-1398 ◽  
Author(s):  
Thiemo Gruber ◽  
Britta Meixner ◽  
Johann Prosser ◽  
Bernhard Sick
Keyword(s):  
Preschool Children ◽  
Support Vector Machines ◽  
Support Vector ◽  
Novel Approach ◽  
Vector Machines

scholarly journals Hyperspectral image classification using support vector machines

2020 ◽  
Vol 9 (4) ◽  
pp. 684
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Bidimensional Empirical Mode Decomposition

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

A novel featureless approach to mass detection in digital mammograms based on support vector machines

2004 ◽  
Vol 49 (6) ◽  
pp. 961-975 ◽  
Author(s):  
Renato Campanini ◽  
Danilo Dongiovanni ◽  
Emiro Iampieri ◽  
Nico Lanconelli ◽  
Matteo Masotti ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Mass Detection ◽  
Vector Machines
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