Pecan Weevil Recognition using Support Vector Machine Method (original) (raw)
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Kernel methods and support vector machines have become the most popular learning from examples paradigms. Several areas of application research make use of SVM approaches as for instance hand written character recognition, text categorization, face detection, pharmaceutical data analysis and drug design. Also, adapted SVM’s have been proposed for time series forecasting and in computational neuroscience as a tool for detection of symmetry when eye movement is connected with attention and visual perception. The aim of the paper is to investigate the potential of SVM’s in solving classification and regression tasks as well as to analyze the computational complexity corresponding to different methodologies aiming to solve a series of afferent arising sub-problems.
Support Vector Machine Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier) Recognition System
2012
Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma ‘σ’ while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for ‘σ’ value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of ‘σ’ for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed system’s success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having ‘σ’ value of 10 is optimal in identifying RPW from an image. The optimal input data for the proposed system needs to be obtained by Regional Properties only.
Radial Basis Polynomial Kernel (RBPK): A Generalized Kernel for Support Vector Machine
Support Vector Machine (SVM) is a novel machine learning method, based on the statistical learning theory and VC (VapnikChervonenkis) dimension concept. It has been successfully applied to numerous classification and pattern recognition problems. Generally, SVM uses the kernel functions, when data is non-linearly separable. The kernel functions map the data from input space to higher dimensional feature space so the data becomes linearly separable. In this, deciding theappropriate kernelfunction for a given application is the crucial issue. This research proposes a new kernel function named ―Radial Basis Polynomial Kernel (RBPK)‖ which combines the characteristics of the two kernel functions: theRadial Basis Function (RBF) kernel and the Polynomial kernel and proves to be better kernel function in comparison of the two when applied individually.The paper proves and makes sure that RBPK confirms the characteristics of a kernel.It also evaluates the performance of the RBPK using Sequential Minimal Optimization (SMO), one of the well known implementation of SVM, against the existing kernels. The simulation uses various classification validation methods viz. holdout, training vs. training, cross-validation and random sampling methods with different datasets from distinct domains to prove the usefulness of RBPK. Finally, it concludes that the use of RBPK results into better predictability and generalization capability of SVM and RBPK can become an alternative generalized kernel.
An empirical assessment of different kernel functions on the performance of support vector machines
Bulletin of Electrical Engineering and Informatics, 2021
Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-today activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM's performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.