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Binary classification problem is one of the mainstream research in pattern recognition field. This study proposed a modified fruit-fly optimization algorithm (FOA), which can find an eligible begin location of the FOA as starting location before running the FOA's procedure, and in the FOA's processing, the SVM parameters is modified by dynamically updating the location of each fruit-fly and the optimal...
People today spend a lot of their time on the internet and a majority of which is spent on surfing various social networks like Facebook, twitter, Instagram etc. So much time is spent online on these social hubs that our opinions, about almost everything seems to be influenced by the larger opinion formed up in these social networks. In this project, opinion mining due to social swarming is discovered...
The importance of the hyper parameters selection for a kernel-based algorithm, viz. Least Squares Support Vector Machines (LSSVM) has been a critical concern in literature. In order to meet the requirement, this work utilizes a variant of Artificial Bee Colony (known as mABC) for hyper parameters selection of LSSVM. The mABC contributes in the exploitation process of the artificial bees and is based...
Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that...
Support Vector Machine (SVM) classification requires set of one or more parameters and these parameters have significant influence on classification precision and generalization ability. Searching for suitable model parameters invokes great computational load, which accentuates with increasing size of the dataset and with amount of the parameters being optimized. In this paper we present and compare...
This parameters selection is an important issue in the research of ??-support vector regression machine (??-SVRM), whose nature is an optimization selection process. Motivated by the effectiveness of differential evolution (DE) algorithm on optimization problem, a new automatic searching method based on DE algorithm was proposed. Experimental results demonstrate that ??-SVRM model optimization based...
To choose an appropriate kernel function is one major task for SVM. Different kernel functions will produce different SVMs and may result in different performances. Combined kernel function shows more stable and higher performance than single kernel function, so there is a need to optimize the combined kernel function to enhance the generalization capability of SVM. This paper proposes to optimize...
SVM performance is very sensitive to the parameter set. In this paper we propose an automatic and effective model selection method. It is based on evolutionary computation algorithms and use recall, precision and error rate estimated by xialpha-estimate as the optimization targets. Optimized by genetic algorithm (GA) or particle swarm optimization (PSO) algorithm, we demonstrate that SVM could automatically...
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