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Feature shaping for linear SVM classifiers
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
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text classification machine learning, feature weighting, feature scaling, SVM Linear classifiers have been shown to be effective for many discrimination tasks. ...
We demonstrate that this pre-processing is beneficial for linear SVM classifiers on a large benchmark of text classification tasks as well as UCI datasets. ...
The goal is to help the classifier induction step by transforming raw feature values into a representation well suited for linear classifiers. ...
doi:10.1145/1557019.1557057
dblp:conf/kdd/FormanSR09
fatcat:gwt4nqloyjgstjblptjpqqcmtu
Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM
2021
Jurnal Teknologi dan Sistem Komputer
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The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %. ...
It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. ...
Based on these results, shape feature extraction-SVM classification obtains the best results using the Linear kernel with 0.04 seconds for computational time. ...
doi:10.14710/jtsiskom.2021.14104
fatcat:ckfjgpy34zbsze4idcqxxg26mu
Detection of the Presence of Safety Helmets on Motorcyclists Using Active Appearance Models
2018
Journal of Intelligent Computing
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This adjustment is measured by differences between AAM shape and appearance parameter vectors, which become the feature vectors for four different classifiers. ...
The degree of adjustment of a new image to the previously trained Active Appearance Model (AAM) acts as the decision criterion for detection. ...
In terms of mean accuracy, the performance of SVM with non-linear polynomial kernel and MLP classifiers are practically the same, with a little advantage for the SVM with non-linear polynomial kernel classifier ...
doi:10.6025/jic/2018/9/4/157-165
fatcat:onx7qjugjna4pcpfjaj326yx3i
A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor
2019
Traitement du signal
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In this paper, feature selector is used along with Histogram of Significant Gradients (HSG) descriptor and linear SVM classifier to enhance the detection accuracy and reduce the processing time. ...
A comparative study has been done employing different classifiers and feature selectors and a system is proposed with better detection capability. ...
SVM classifier with and without various feature selectors Classifier
TP
TN
FP
FN
SVM Linear
560
1065
17
179
SVM Linear with
mRMR
645
1044
38
94
SVM Linear with
JMI
635
1048
34 ...
doi:10.18280/ts.360110
fatcat:zukprn5lyvcuvofhnu6hzwmwe4
PERFORMANCE ANALYSIS OF FRUIT CROP FOR MULTICLASS SVM CLASSIFICATION
2020
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES
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The Experiments was carried out to find the best SVM kernel among linear, cubic and Gaussian for fruit categorization. ...
It is measured by analyzing the different mention kernel selection on color and shape features. Two coding design method such as one-vs.-one and one-vs. ...
/Model
Kernel
technique
Dataset
Features
Accuracy
1
[XIX]
Multi class SVM
-
-
Color and
Shape
92.22%
2
[XV]
Multiclass SVM-
Linear
Fruit 360
Color
,Shape
98-100%
3
Proposed
work ...
doi:10.26782/jmcms.2020.08.00015
fatcat:hzo554qzefhhzcprbnqsir3u4i
Face shape classification using Inception v3
[article]
2019
arXiv
pre-print
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(LDA), support vector machines with linear kernel (SVM-LIN), support vector machines with radial basis function kernel (SVM-RBF), artificial neural networks or multilayer perceptron (MLP), and k-nearest ...
Results show that training accuracy and overall accuracy ranges from 98.0% to 100% and from 84.4% to 84.8% for Inception v3 and from 50.6% to 73.0% and from 36.4% to 64.6% for the other classifiers depending ...
Five classifiers were trained and tested using features obtained from 500 images: linear discriminant analysis (LDA), support vector machines with linear (SVM-LIN) and radial basis function kernels (SVM-RBF ...
arXiv:1911.07916v1
fatcat:5hhmq5izjvgmtlcn7i4nzekrou
Poetry Classification Using Support Vector Machines
2012
Journal of Computer Science
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We used tfidf for both classification experiments and the shape feature for the classification of poetry and non-poetry experiment alone. ...
The capability of SVM through Radial Basic Function (RBF) and linear kernel function are implemented to classify pantun by theme, as well as poetry or non-poetry. ...
In this case, meanings of poetry give better features for the SVM classifier. Table 5 . ...
doi:10.3844/jcssp.2012.1441.1446
fatcat:ogzqzt46pnfphkktftqyd6hwgq
People recognition and pose estimation in image sequences
2000
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
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The person in the image is represented by a set of features based on color and shape information. ...
Recognition is carried out through a hierarchy of biclass SVM classifiers that are separately trained to recognize people and estimate their poses. ...
The core of the system is a multiclass classification problem which we have approached using two types of hierarchical SVM classifiers. ...
doi:10.1109/ijcnn.2000.860771
dblp:conf/ijcnn/NakajimaPP00
fatcat:7k67peaffbfwdahxn55gp56svy
Benign and Malignant Dermatoscopy Image Classification
2020
2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)
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Dermatologists use various techniques for diagnosing the malignant, in which the popular and reliable clinical method is dermatoscopy. ...
Thus recently, image processing and machine learning algorithms have been applied for the accurate diagnoses of skin cancers from the dermatoscopic images. ...
Using ReliefF algorithm, the non-linear SVM with polynomial kernel of order3 outperformed all other SVM classifiers with 90% accuracy, 91% sensitivity, 86% specificity, and 93% precision. ...
doi:10.1109/icaecc50550.2020.9339500
fatcat:r4zpogqlfneebne4bjkmskumfq
A Comparative Study of Feature Extraction Methods in Images Classification
2015
International Journal of Image Graphics and Signal Processing
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We analyze the models obtained by each feature extraction method under each classifier. ...
However, in this paper, we present a comparison protocol of several feature extraction techniques under different classifiers. ...
Shape Features Shape features are very used in the literature (in object recognition and shape description). ...
doi:10.5815/ijigsp.2015.03.03
fatcat:jwz3f4jtsffhjholmk74qik45y
Morphometric Analysis of Brain Structures for Improved Discrimination
[chapter]
2003
Lecture Notes in Computer Science
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Spherical harmonics technique and point distribution model are used for shape description. ...
Classification is performed using linear discriminants and support vector machines with several feature selection approaches. ...
We thank Martin Styner and Hany Farid for valuable discussions, and Laura Flashman for data preparation. ...
doi:10.1007/978-3-540-39903-2_63
fatcat:xdhgz4exnfhz3pz75t2fjxvg3i
Improving the Performance of Human Detection Technique using Cascaded Support Vector Machine
2015
International Journal of Computer Applications
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The classification process define the pattern of feature for the process of detection, the process of features generates a bag of feature for the process of classification technique. ...
The process of human detection is very complex due to variant feature of human such as color, texture and shape and size. ...
training data by using linear SVM. ...
doi:10.5120/20531-2872
fatcat:5xbu5tfmxveuhhcpse4woge3ey
PLANT LEAF RECOGNITION SYSTEM USING KERNEL ENSEMBLE APPROACH
2018
International journal of advances in signal and image sciences
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The classification is done by ensemble approach with different SVM kernels like Linear (SVM-L), Radial basis function (SVM-R), Polynomial (SVM-P) and Quadratic (SVM-Q). ...
Finally, the outputs of each SVM classifier are fused to classify plant leaf images. The PLR system is carried on using Folio database that contains 640 leaf images captured from 32 species. ...
Shape features for PLR is described in [2] using neural network. The moment invariant features are used as shape features to classify three plants. ...
doi:10.29284/ijasis.4.1.2018.30-36
fatcat:pd3mlgbbe5c27i7kw2uwnm2xuu
Two-Stage Traffic Sign Detection and Recognition Based on SVM and Convolutional Neural Networks
2019
IET Image Processing
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The first stage aims to detect and classify the detected traffic signs into circular and triangular shape using HOG features and linear support vector machines (SVMs). ...
Furthermore, the average processing time demonstrates its suitability for real-time processing applications. ...
After image filtering, circular and triangular shapes were classified by the linear SVM with HOG features. ...
doi:10.1049/iet-ipr.2019.0634
fatcat:4lxutq6rivfhlin7lhetvo6wqu
Robust facial expression classification using shape and appearance features
2015
2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR)
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2015 pre-print arXiv:1505.04030v1 fatcat:owh575wjsfdnfcfsvjvdxtsgy4
By using linear discriminant analysis, the dimensionality of the feature is reduced which is further classified by using the support vector machine (SVM). ...
The use of small parts of face instead of the whole face for extracting features reduces the computational cost and prevents the over-fitting of the features for classification. ...
Jeffery Cohn for providing the Cohn-Kanade database, and Dr. Michael J. Lyons for providing JAFFE database. ...
doi:10.1109/icapr.2015.7050661
dblp:conf/icapr/HappyR15
fatcat:63bacjoqq5ez5lj43dfvwvu3mu
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