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Support vector machine (SV machine, SVM) is a genius invention with many merits, such as the non-existence of local minima, the largest separating margins of different clusters, as well as the solid theoretical foundation. However, it is also well-noted that SVMs are frequently with a large number of SVs. In this paper, we investigate the number of SVs in a benchmark problem, the parity problem experimentally...
A multi-layer adaptive optimizing parameters algorithm is developed for improving least squares support vector machines (LS-SVM), and a military equipment intelligent cost estimation model is proposed based on the optimized LS-SVM. The intelligent cost estimation process is divided into three steps in the model. In the first step, cost-drive-factor is needed to be selected, which is significant for...
By considering the geometric properties of the Support Vector Machine (SVM) and Minimal Enclosing Ball (MEB) optimization problems, we show that upper and lower bounds on the radius-margin ratio of an SVM can be efficiently computed at any point during training. We use these bounds to accelerate radius-margin parameter selection by terminating training routines as early as possible, while still obtaining...
This paper proposes a new music genre classification algorithm based on dynamic music frame analysis and support vector machine (SVM). The dynamic music frame analysis could cover the long-term and the short-term music genre features which can represent the time-varying behavior of music signals. The music genre features used in this paper are mel-frequency cepstral coefficient (MFCC) and log energy...
Exact segmentation of fingerprint image is very important for fingerprint singular points and minutiae features extraction. In this paper, a method for fingerprint image segmentation is proposed based on Support Vector Machine (SVM). The fingerprint image is broken into 16*16 prospects blocks and background blocks. The block average gray, block gray variance, block contrast and the largest peak of...
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...
Aimed at the research on freeway detection algorithm has great significance for improving efficiency and effectiveness of freeway traffic management, this paper based on the freeway traffic flow's characteristics, in accordance with the incident detection's basic principle, researches on freeway incident detection based on Support Vector Machine (SVM). This paper designs four different simulation...
Support vector machine (SVM) algorithm has shown a good learning ability and generalization ability in classification, regression and forecasting. This paper mainly analyzes the the performance of support vector machine algorithm in the classification problem, including the algorithm in the kernel function selection, parameter optimization, and integration of other algorithms and to deal with multi-classification...
In the framework of remote-sensing image classification support vector machines (SVMs) have recently been receiving a very strong attention, thanks to their accurate results in many applications and good analytical properties. However, SVM classifiers are intrinsically noncontextual, which represents a severe limitation in image classification. In this paper, a novel method is proposed to integrate...
Scaled Convex Hulls (SCHs) have been recently proposed by Liu et al. as the basis of a method to build linear classifiers that, when extended to kernel settings, provides an alternative approach to more established methods such as SVMs. Here we show how to adapt the Mitchell-Dem'yanov-Malozemov (MDM) algorithm to build such SCH-based classifiers by solving a concrete nearest point problem. We shall...
This study concerns with the diagnosis of composite defects using pitch-catch method in aircraft material by applying the Wavelet transform (WT) analysis, PCA along with support vector machine (SVM). A novel application is presented exploring the problem of detection and estimation of the various defects; the early detection and classification of aircraft defects is of particular importance, as the...
Sequential Minimal Optimization (SMO) algorithm is very effective when solving large-scale support vector machine (SVM). The existing algorithms need to judge which quadrant the 4 Lagrange multipliers lie in, complicating its implementation. In addition, the existing algorithms all assume that the kernel functions are positive definite or positive semidefinite, limiting their applications. Having...
The classification of mental tasks is one of the key issues of Brain Computer Interface (BCI). Owing to its powerful capacity in solving non-linearity problems, Support Vector Machine (SVM) has been widely used in classification. Traditional SVM, however, assumes that each feature of a sample contributes equally to classification accuracy, which is not necessarily true in real world applications....
In order to improve the training efficiency to the data set, an improved adaptive Support Vector Machine (SVM) algorithm with combinational Fuzzy C-means Clustering is proposed. With multi-layer fuzzy C-means clustering algorithm original data are pretreated to remove the training data, which has no contribution to the classification. The remaining data are used to complete the training work for SVM...
In the traditional feature selection, only a simple feature selection can be made, which will lead to the loss of information. In this paper, the requirement of weapon system economic analysis on the cost forecasting and the importance analysis of tactical and technical indicators were taken into account, moreover, considering the shortcomings of the traditional method of feature selection. A weighted...
Accurate prediction of splice sites in DNA sequences is a challenging problem in bioinformatics. The splice site prediction still faces many tough challenges, and above all is that it is not clear how many and which features are relevant with the splicing process. So feature selection is often used to improve the prediction accuracy, and it will also provide us with useful biological knowledge. On...
Support vector machine is the highlight in machine learning. Also, performance evaluation and parameters selection for SVM model become an important issue to make it practically useful. In this paper, after investigating current evaluation index for pattern recognition, we introduced receiver operating characteristic curve into the performance evaluation. Area under receiver operating characteristic...
This study proposes a new strategy combining with the SVM(support vector machine) classifier for features selection that retains sufficient information for classification purpose. Our proposed approach uses F-score models to optimize feature space by removing both irrelevant and redundant features. To improve classification accuracy, the parameters optimization of the penalty constant C and the bandwidth...
To establish suitable models to describe the behavior of biochemistry systems, a new modeling method was introduced, combining multiple objective ant colony optimization(MOACO) with the dynamic Epsilon-SVM. The hyper-parameters of Epsilon-SVM were automatically decided by using multiple objective ant colony optimization(MOACO). Each training sample used different error. The model for penicillin production's...
Time-cost optimization problem is one of the most important aspects of construction project planning and control. And the relationship between direct cost and activity duration is the base of project planning and control. Nowadays, when construction planners made an optimization of time-cost, they assumed the rate of direct cost is linear in order to calculate it simply. But in fact the direct cost...
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