scholarly journals Minimal Complexity Support Vector Machines for Pattern Classification

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 88
Author(s):  
Shigeo Abe
Keyword(s):  
Dual Problem ◽  
Support Vector ◽  
Vc Dimension ◽  
Regularization Term ◽  
Standard Support Vector Machine ◽  
Vector Machines ◽  
Benchmark Datasets ◽  
Dual Variables ◽  
Standard Support ◽  
Better Than

Minimal complexity machines (MCMs) minimize the VC (Vapnik-Chervonenkis) dimension to obtain high generalization abilities. However, because the regularization term is not included in the objective function, the solution is not unique. In this paper, to solve this problem, we discuss fusing the MCM and the standard support vector machine (L1 SVM). This is realized by minimizing the maximum margin in the L1 SVM. We call the machine Minimum complexity L1 SVM (ML1 SVM). The associated dual problem has twice the number of dual variables and the ML1 SVM is trained by alternatingly optimizing the dual variables associated with the regularization term and with the VC dimension. We compare the ML1 SVM with other types of SVMs including the L1 SVM using several benchmark datasets and show that the ML1 SVM performs better than or comparable to the L1 SVM.

Analysis for Incremental and Decremental Standard Support Vector Machine

2014 ◽  
Vol 24 (7) ◽  
pp. 1601-1613 ◽  
Author(s):  
Bin GU ◽  
Guan-Sheng ZHENG ◽  
Jian-Dong WANG
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Standard Support Vector Machine ◽  
Standard Support

scholarly journals Contact Lens Classification by Using Segmented Lens Boundary Features

2018 ◽  
Vol 11 (3) ◽  
pp. 1129
Author(s):  
Nur Ariffin Mohd Zin ◽  
Hishammuddin Asmuni ◽  
Haza Nuzly Abdul Hamed ◽  
Razib M. Othman ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Contact Lens ◽  
State Of The Art ◽  
Classification Method ◽  
Support Vector ◽  
Local Descriptors ◽  
Iris Image ◽  
Vector Machines ◽  
False Reject Rate ◽  
Better Than

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.

scholarly journals A Novel Sparse Least Squares Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao-Lei Xia ◽  
Weidong Jiao ◽  
Kang Li ◽  
George Irwin
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Regularization Parameter ◽  
Support Vector ◽  
Training Algorithm ◽  
Least Squares Approximation ◽  
Generalization Ability ◽  
Vector Machines ◽  
Training Cost ◽  
Benchmark Datasets

The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness. The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function. This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm—the Forward Least Squares Approximation SVM (FLSA-SVM). A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost. The FLSA-SVMs can also detect the linear dependencies in vectors of the input Gramian matrix. These attributes together contribute to its extreme sparseness. Experiments on benchmark datasets are presented which show that, compared to various SVM algorithms, the FLSA-SVM is extremely compact, while maintaining a competitive generalization ability.

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

scholarly journals HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Rianon Zaman ◽  
Shahana Yasmin Chowdhury ◽  
Mahmood A. Rashid ◽  
Alok Sharma ◽  
Abdollah Dehzangi ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Prediction Method ◽  
Dna Binding Protein ◽  
Support Vector ◽  
Classification Technique ◽  
Protein Prediction ◽  
Wide Range ◽  
Vector Machines ◽  
Benchmark Datasets

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

scholarly journals Leveraging machine learning and self-administered tests to predict COVID-19:An olfactory and gustatory dysfunction assessment through crowd-sourced data in India

2021 ◽  
Author(s):  
Ritesh Kumar ◽  
Maneet Singh ◽  
Prateek Singh ◽  
Valentina Parma ◽  
Kathrin Ohla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Online Survey ◽  
Lemon Juice ◽  
Taste Perception ◽  
Support Vector ◽  
Health Issues ◽  
Home Test ◽  
Vector Machines ◽  
Perception Of Self ◽  
Better Than

It has been established that smell and taste loss are frequent symptoms during COVID-19 onset. Most evidence stems from medical exams or self-reports. The latter is particularly confounded by the common confusion of smell and taste. Here, we tested whether practical smelling and tasting with household items can be used to assess smell and taste loss. We conducted an online survey and asked participants to use common household items to perform a smell and taste test. We also acquired generic information on demographics, health issues including COVID-19 diagnosis, and current symptoms. We developed several machine learning models to predict COVID-19 diagnosis. We found that the random forest classifier consistently performed better than other models like support vector machines or logistic regression. The smell and taste perception of self-administered household items were statistically different for COVID-19 positive and negative participants. The most frequently selected items that also discriminated between COVID-19 positive and negative participants were clove, coriander seeds, and coffee for smell and salt, lemon juice, and chillies for taste. Our study shows that the results of smelling and tasting household items can be used to predict COVID-19 illness and highlight the potential of a simple home-test to help identify the infection and prevent the spread.

scholarly journals Remote sensing tree classification with a multilayer perceptron

2018 ◽  
Author(s):  
G Rex Sumsion ◽  
Michael S Bradshaw ◽  
Kimball T Hill ◽  
Lucas D G Pinto ◽  
Stephen R Piccolo ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Tree Species ◽  
Scientific Progress ◽  
Training Data ◽  
Support Vector ◽  
Species Classification ◽  
Community Based ◽  
Perceptron Algorithm ◽  
Vector Machines ◽  
Better Than

To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We predicted tree species and genus at the pixel level using hyperspectral and LIDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multilayer perceptron. At the pixel level, the multilayer perceptron algorithm predicted species or genus with high accuracy (92.7 and 95.9%, respectively) on the training data and performed better than the other algorithms (85.8-93.5%). This indicates promise for the use of the MLP algorithm for tree-species classification and coincides with a growing body of research in which neural network-based algorithms outperform other types of classification algorithms for machine vision. To aggregate patterns across the images, we used an ensemble approach that averages the pixel-level outputs of the MLP algorithm to predict species at the crown level. The accuracy of these predictions on the test set was 68.8% for species.

scholarly journals A Method for Greenhouse Temperature Prediction Based on XGBoost Algorithm and Linear Residual Model

CONVERTER ◽  
2021 ◽  
pp. 108-121
Author(s):  
Huijin Han, Et al.
Keyword(s):  
Mean Squared Error ◽  
Prediction Method ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Small Scale ◽  
Temperature Prediction ◽  
Precise Control ◽  
Squared Error ◽  
Vector Machines ◽  
Better Than

Temperature prediction is significant for precise control of the greenhouse environment. Traditional machine learning methods usually rely on a large amount of data. Therefore, it is difficult to make a stable and accurate prediction based on a small amount of data. This paper proposes a temperature prediction method for greenhouses. With the prediction target transformed to the logarithmic difference of temperature inside and outside the greenhouse,the method first uses XGBoost algorithm to make a preliminary prediction. Second, a linear model is used to predict the residuals of the predicted target. The predicted temperature is obtained combining the preliminary prediction and the residuals. Based on the 20-day greenhouse data, the results show that the target transformation applied in our method is better than the others presented in the paper. The MSE (Mean Squared Error) of our method is 0.0844, which is respectively 20.7%, 76.0%, 10.2%, and 95.3% of the MSE of LR (Logistic Regression), SGD (Stochastic Gradient Descent), SVM (Support Vector Machines), and XGBoost algorithm. The results indicate that our method significantly improves the accuracy of the prediction based on the small-scale data.

scholarly journals Earth remote sensing imagery classification using a multi-sensor super-resolution fusion algorithm

2020 ◽  
Vol 44 (4) ◽  
pp. 627-635
Author(s):  
A.M. Belov ◽  
A.Y. Denisova
Keyword(s):  
Remote Sensing ◽  
Support Vector Machines ◽  
Random Forest ◽  
Classification Accuracy ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Earth Remote Sensing ◽  
Vector Machines ◽  
Fused Image ◽  
Better Than

Earth remote sensing data fusion is intended to produce images of higher quality than the original ones. However, the fusion impact on further thematic processing remains an open question because fusion methods are mostly used to improve the visual data representation. This article addresses an issue of the effect of fusion with increasing spatial and spectral resolution of data on thematic classification of images using various state-of-the-art classifiers and features extraction methods. In this paper, we use our own algorithm to perform multi-frame image fusion over optical remote sensing images with different spatial and spectral resolutions. For classification, we applied support vector machines and Random Forest algorithms. For features, we used spectral channels, extended attribute profiles and local feature attribute profiles. An experimental study was carried out using model images of four imaging systems. The resulting image had a spatial resolution of 2, 3, 4 and 5 times better than for the original images of each imaging system, respectively. As a result of our studies, it was revealed that for the support vector machines method, fusion was inexpedient since excessive spatial details had a negative effect on the classification. For the Random Forest algorithm, the classification results of a fused image were more accurate than for the original low-resolution images in 90% of cases. For example, for images with the smallest difference in spatial resolution (2 times) from the fusion result, the classification accuracy of the fused image was on average 4% higher. In addition, the results obtained for the Random Forest algorithm with fusion were better than the results for the support vector machines method without fusion. Additionally, it was shown that the classification accuracy of a fused image using the Random Forest method could be increased by an average of 9% due to the use of extended attribute profiles as features. Thus, when using data fusion, it is better to use the Random Forest classifier, whereas using fusion with the support vector machines method is not recommended.

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