Comparing support vector machines with Gaussian kernels to radial basis function classifiers (original) (raw)
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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.
Performance Evaluation of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF
International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), 2019
The importance of character recognition cannot be over emphasized. It finds applications in many automated system. In most cases these applications require high precision (e.g. automatic grading system, document digitization, license plate recognition systems, e.t.c) as well as low resource overhead. However, these are conflicting requirements, because the more the precision required, the more computation needed hence the more increase in resource overhead. In the research, two classification algorithms in Artificial Neural Networks (ANN): Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were applied to handwritten digit recognition and their performance is investigated. The duo were compared in terms of resources requirement for training and accuracy. It is found that MLP-NN is much faster to train (5.5min) compared to RBF (50.0min). However, during testing it is found that both have an accuracy of ≈ 95%.
International Journal on Artificial Intelligence Tools, 2015
Classification is one of the most fundamental and formidable tasks in many domains including biomedical. In biomedical domain, the distributions of data in most of the datasets into predefined number of classes is significantly different (i.e., the classes are distributed unevenly). Many mathematical, statistical, and machine learning approaches have been developed for classification of biomedical datasets with a varying degree of success. This paper attempts to analyze the empirical performance of two forefront machine learning algorithms particularly designed for classification problem by adding some novelty to address the problem of imbalanced dataset. The evolved radial basis function network with novel kernel and support vector machine with mixture of kernels are suitably designed for the purpose of classification of imbalanced dataset. The experimental outcome shows that both algorithms are promising compared to simple radial basis function neural networks and support vector m...
RECOGNITION OF HANDWRITTEN DIGITS USING RBF NEURAL NETWORK
IJRET, 2013
Pattern recognition is required in many fields for different purposes. Methods based on Radial basis function (RBF) neural networks are found to be very successful in pattern classification problems. Training neural network is in general a challenging nonlinear optimization problem. Several algorithms have been proposed for choosing the RBF neural network prototypes and training the network. In this paper RBF neural network using decoupling Kalman filter method is proposed for handwritten digit recognition applications. The efficacy of the proposed method is tested on the handwritten digits of different fonts and found that it is successful in recognizing the digits
Identification of handwritten digits is one of the major areas of research in the field of character recognition. Artificial Neural Networks helps in computer vision that deals with how a computer could achieve high-level of understanding from digital images or videos. Thus, neural networks prove to be a boon in recognizing handwritten digits that are scanned as images. However, this paper aims at studying the working of specifically three neural networks-Multi-Layered Perceptron (MLP), Radial Basis Function (RBF) and Convolutional Neural Network (CNN). In order to focus majorly on the implementation of these three neural networks rather than the complexity of the dataset being used, we have used MNIST (Modified National Institute of Standard and Technology) dataset from keras library. The MNIST dataset contains 70,000 black and white images of handwritten English digits (60,000 training images and 10,000 testing images). In our study of the above three mentioned neural networks, we have used relu as activation function in the hidden layers and softmax as activation function in the final layer of neural network, Adam as an optimizer and cross-entropy as loss function. We have observed that all three networks give accuracy above 95%, however, the major difference is in its training time and error rate.