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Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM)
[chapter]
1998
Face Recognition
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The accuracy observed on test data, using both polynomials of degree 3 and Radial Basis Functions (RBFs) as kernel approximation functions, to determine the SVM separating hyperplanes, has been 100%. ...
The best generalization performance of 4% was achieved using polynomial kernels of second degree as the set of approximating functions. ...
as D x
Figure 1 . 1 Examples for (a) training images and (b) test images with three posesWe report now on two SVM experiments using as approximating functions polynomial and RBF kernels, respectively ...
doi:10.1007/978-3-642-72201-1_32
fatcat:qagv4l6vxrh5bafzbkagykzzji
Explaining Support Vector Machines: A Color Based Nomogram
2016
PLoS ONE
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Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. ...
For other kernels an indication of the reliability of the approximation is presented. ...
The two SVM models with optimal tuning parameters are: a first SVM model with an RBF kernel (γ = 2 −4 and C = 10 5 ) and a second SVM model with a polynomial kernel (δ = 4 and C = 100). ...
doi:10.1371/journal.pone.0164568
pmid:27723811
pmcid:PMC5056733
fatcat:clwqqcntcnasvmlfj3f46xsgoq
Evaluation of kernel methods for speaker verification and identification
2002
IEEE International Conference on Acoustics Speech and Signal Processing
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We compare the polynomial kernel, the Fisher kernel, a likelihood ratio kernel and the pair hidden Markov model kernel with baseline systems based on a discriminative polynomial classifier and generative ...
Gaussian mixture model classifiers. ...
Polynomial classifier The polynomial classifier method operates in a similar way to the SVM with a polynomial kernel. ...
doi:10.1109/icassp.2002.5743806
dblp:conf/icassp/WanR02
fatcat:znubnb3ujzer5fywf3sbhqqsye
Evaluation of kernel methods for speaker verification and identification
2002
IEEE International Conference on Acoustics Speech and Signal Processing
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We compare the polynomial kernel, the Fisher kernel, a likelihood ratio kernel and the pair hidden Markov model kernel with baseline systems based on a discriminative polynomial classifier and generative ...
Gaussian mixture model classifiers. ...
Polynomial classifier The polynomial classifier method operates in a similar way to the SVM with a polynomial kernel. ...
doi:10.1109/icassp.2002.1005828
fatcat:i5y27c5aszgjve3acxrnwcqnia
Benchmarking Least Squares Support Vector Machine Classifiers
2004
Machine Learning
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In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. ...
Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. ...
LS-SVM classifiers with polynomial and linear kernel yield the best performance on two and one datasets, respectively. ...
doi:10.1023/b:mach.0000008082.80494.e0
fatcat:znrnnkohxndpjm7fsc4lgreafy
Discriminative tree-based feature mapping
2013
Procedings of the British Machine Vision Conference 2013
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Rather than finding a kernel with these properties and approximating it, we directly learn a non-linear decision boundary using boosted decision trees. ...
Rather that trying to approximate a kernel, we present explicit approximate mappings of tree-based classifiers learnt specifically for a particular problem and feature set. ...
Rather than finding a kernel with these properties and approximating it, we directly learn a non-linear decision boundary using boosted decision trees. ...
doi:10.5244/c.27.71
dblp:conf/bmvc/KobetskiS13
fatcat:arrap5gssfbq7j5o5onrdth3wu
Classification of Histological Images Based on the Stationary Wavelet Transform
2015
Journal of Physics, Conference Series
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We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels. ...
The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. ...
Three different kernel types (Linear, RBF and Polynomial) were applied with our method. Its diagram is depicted in Figure 1 . ...
doi:10.1088/1742-6596/574/1/012133
fatcat:yzafgrupaffxjpzzvszrruhtqe
Robust feature induction for support vector machines
2004
Twenty-first international conference on Machine learning - ICML '04
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The comparison with an SVM model using nonlinear kernels also indicates that this approach is effective and robust, particularly when the number of training data is small. ...
We present empirical studies with discussion to show that this approach is effective in improving classification accuracy for SVMs. ...
Experiment II: Comparison with Nonlinear Kernels We apply both polynomial kernels and the RBF kernel to introduce nonlinear features for SVMs. ...
doi:10.1145/1015330.1015370
dblp:conf/icml/JinL04
fatcat:im6n7esugbcqtayxv7l7ni76oe
A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule
2004
Pattern Recognition Letters
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Using synthetic and real data, the classification technique is shown to be more robust to kernel conditions with a significantly lower computational cost than conventional SVMs. ...
Support Vector Machines (SVMs) are by far the most sophisticated and powerful classifiers available today. However, this robustness and novelty in approach come at a large computational cost. ...
In terms of providing a consistent classifier accuracy for various choices of kernel parameters, guaranteed convergence and fast computation, the NNSRM method clearly outperforms the SVM classifier. ...
doi:10.1016/j.patrec.2003.09.002
fatcat:oupur7ofi5bt7fokuapyzwonuu
Analyzing Potential of SVM Based Classifiers for Intelligent and Less Invasive Breast Cancer Prognosis
2010
2010 Second International Conference on Computer Engineering and Applications
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Sensitivity, specificity and accuracy parameters along with RoC curves have been used to explain the performance of each SVM algorithm with different kernel types. ...
Experiments were performed using different types of SVM algorithms analyzing their classification efficiency using different kernel parameters. ...
Application of SVM classifiers (C-SVM and nu-SVM) Using RBF kernel, Polynomial and sigmoid separately with each SVM classifier Training the model on the training data. ...
doi:10.1109/iccea.2010.212
fatcat:vzggubifsbfzfe3sbisweo3mhe
Comparative Study of different Methodologies to Predict Human Character
2015
International Journal of Applied Information Systems
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To exhibit the method and the value of modeling human performance with SVM, SVM applied to a real world human factors problem of identification of character of a person. ...
These attributes are subsequently provided to Neural classifier and into support vector machine for categorization. ...
SVM used for human character predicting systems as a function of image based metrics and compared the SVM based model with other models developed with linear kernel, polynomial kernel, and ANN. ...
doi:10.5120/ijais15-451301
fatcat:qts6n4f7xneixccumsdu5sd4ey
A Comparative Study in Kernel-Based Support Vector Machine of Oil Palm Leaves Nutrient Disease
2012
Procedia Engineering
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In this paper, Support Vector Machine (SVM) is evaluated as classifier with three different kernels namely linear kernel, polynomial kernel with soft margin and polynomial kernel with hard margin. ...
Based on the best performance result, polynomial kernel with soft margin is capable of classifying nutrient diseases accurately in the oil palm leaves with accuracy of 95% of correct classification. ...
Gratitude and appreciation to Malaysia Palm Oil Board (MPOB) also for advises given and Rohani Oil Palm Plantation for research subjects provided. ...
doi:10.1016/j.proeng.2012.07.321
fatcat:edpqmd5ncbfzfiukt267utzcbe
Inflammatory Bowel Disease Classification Using Neural Network and Support Vector Machine
2020
Zenodo
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and 79.9% respectively; however, SVM with Radial Basis Function (RBF) kernel outperforms ANN with accuracy of 70.9% and 67% respectively. ...
Moreover, using different datasets containing normal and IBD patients, the experiment result reveals that ANN and SVM with linear kernel have comparable performance in classification with accuracy of 80% ...
In terms of accuracy [ Fig. 4 ], SVM with Linear kernel achieved 79.07% accuracy, 2.37% higher than SVM with RBF kernel, while SVM with polynomial kernel with the lowest with 44.1%. ...
doi:10.5281/zenodo.3712093
fatcat:igrhnxkpqzbonfr6j2qubi3vcq
SVM Classification of Neonatal Facial Images of Pain
[chapter]
2006
Lecture Notes in Computer Science
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Whereas SVM with polynomial kernel of degree 3 obtained the best classification score (88.00%) using the first evaluation protocol, SVM with a linear kernel obtained the best classification score (82.35% ...
However, experiments reported here indicate no significant difference in performance between linear and nonlinear kernels. ...
SVMs with five kernels (linear, RBF, polynomial degree 2, polynomial degree 3, and polynomial degree 4) were assessed using protocols A and B. ...
doi:10.1007/11676935_15
fatcat:s3i3ji3uovb2pa2hc6qkt4lfhe
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. ...
Results show that this approach is feasible and may be recommended. ...
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
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