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Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
A method to determine C,γ , the hyper-parameters, range for Radial Basis Function Support Vector Machines (RBF SVMs) is proposed. The γ range is determined by the extreme Squared Euclidean Distance (SED) quantiles of the training set, and the C range is determined by one pass whole training set training decreasingly along logγmax to the over-regularized limit first and increasingly along logγmedian...
This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error...
In this paper, we present the problem of appropriate feature selection for constructing a Maximum Entropy (ME) based Named Entity Recognition (NER) system under the multiobjective optimization (MOO) framework. Two conflicting objective functions are simultaneously optimized using the search capability of MOO. These objectives are (i). the dimensionality of features, which is tried to be minimized,...
A method for recognizing the emotion states of subjects based on 30 features extracted from their Galvanic Skin Response (GSR) signals was proposed. GSR signals were acquired by means of experiments attended by those subjects. Next the data was normalized with the calm signal of the same subject after being de-noised. Then the normalized data were extracted features before the step of feature selection...
This paper deals with the study of a water quality prediction model through application of LS-SVM in Liuxi River in Guangzhou. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome...
The feature subset selection is a key preprocessing part in the detection of the stored-grain insects based on the image recognition technology. According to the global optimization ability of the particle swarm optimization (PSO) and the superior classification performance of the support vector machines (SVM), this study proposed a method based on PSO and SVM to improve the classification accuracy...
In order to recognize stratums, a new support vector machine model (SVMM) is built on the basis of well-logging data and with RBF as its kernel function. Through the optimization of penalty parameter C and the introduction of a discriminant function, the classification accuracy of SVMM is greatly enhanced. Experiments show that the SVM classifier can be applied effectively to the recognition of stratums,...
Nozzle plays very important role to control the gas flow during the interruption for SF6 circuit breaker (CB). Due to the higher non-linear global mapping relationship between interruption performance of SF6 CB and its nozzle structural parameters, artificial neural network (ANN) and genetic algorithm (GA) were applied to the nozzle parameter optimization of SF6 CB on the basis of the non-linear mapping...
A new method for the optimal design of the electromagnetic devices is presented. The method utilizes artificial neural networks (ANNs) in a design environment which encompasses numerical computations and expert's input for generating a variety of ANN training data. Results of two implementation examples are provided. The optimal design is obtained quickly (in a matter of milliseconds) once the ANNs...
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