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this paper presents a classification based on support vector machine (SVM) to carry out comprehensive analysis of the ability of enterprises paying debt,reduce the risk of bank to provide a loan. First this paper introduces the main principle of support vector machines to establish data classification model, using historical data for classification. Then collect the financial indices of 80 enterprises...
In this paper we propose a novel Support Vector Machine(SVM) based approach for noisy data removal from datasets. It is observed that the instability present in the dataset greatly affects the overall performance of the any classifier. Hence, we propose a methodology for removal of such instabilities. In the proposed approach, we proceed by determining the clusters formed using support equilibrium...
It's still in search that how to apply SVM in muti-classify. Directed Acyclic Graph is easier to be computed and has better learning effect than other arithmetic. Experimental platform is used to simulate typical faults of circumrotate machines. Based on the frequency domain feature, energy eigenvector of frequency domain is presented using wavelet packet analysis method. DAGSVM is applied in classification,...
Support Vector Machine (SVM) is an effective classifier for classification task, but a vital shortcoming of SVM is that it needs huge computation for large scale learning tasks. Sample selection is a feasible strategy to overcome the problem. In order to rapidly reduce training samples without sacrificing recognition accuracy, this paper presents a novel sample selection strategy based on subspace...
Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis etc for the classification and regression. This paper emphasizes the classification task with Support Vector Machine...
Protein classification plays an important role in the research in Bioinformatics. Many discriminative methods, including the SVM based algorithms are used to do this job. In order to use these methods, variable length protein sequences must be converted into fixed-length dimensional vectors. The current work presents a new method of converting sequences into vectors. The method first constructs profile...
Current SVM-based image steganographic detection algorithmins haven't considered the impact of specific data, and the choice of the parameter greatly affects the classification performance, it's necessary to constructing the kernel function from the perspective of specific data. This paper proposes a steganographic detection method for JPEG image that base on the data-dependent concept, first obtain...
An optimized multi-class classification algorithm based on SVM decision tree (SVMDT) is proposed. But by SVMDT, the generalization ability depends on the tree structure. In this paper, the relativity separability measure between classes is defined based on the distribution of the training samples to improve the generalization ability of SVMDT. SVM is extended to non-linear SVM by using kernel functions...
In order to overcome the situation that interactions exist between all classifiers from multiple classification system. In this study, we fuse the multiple SVM classifiers by fuzzy fusion algorithm with respect to a novel composed measure of L-measure and Delta (δ)-measure. We expect to gain a more accurate classification than single SVM and other combination method, like majority vote. From the experiment...
Computational complexity is one of the most important issues in any machine-learning algorithm. A novel working set selection mechanism is proposed to improve Support Vector Machine (SVM) learning. Implementation is based on the Keerthi et al.'s SMO algorithm, but our approach is one-class classification. When selecting samples for the optimization process, much effort is spent to find the most violating...
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...
Decision-tree-based multiclass support vector machine (DTSVM) can solve the problem of unclassified regions that exists in the conventional SVM. But the classification precision and generalization ability of DTSVM classifier depends on the structure of the decision tree. In addition, the training speed of DTSVM becomes slower for more training samples. In this paper, a new measurement of inter-class...
Based on the framework of support vector machines (SVM) using one against one (OAO) strategy, a new kernel method based on Bhattacharyya distance is proposed to raise the classification accuracy by combining the characteristics of hyperspectral data. The proposed method takes advantage of the non-uniform information distribution of hyperspectral data and makes the band with greater separability play...
Based on the text information processing, we have made a study on the application of support vector machine in text categorization. Through introducing the basic principle of SVM, we described the process of text classification and further proposed a SVM-based classification model. Finally, experimental data show that F1 value of SVM classifier has reached more than 86.26%, and the classification...
Automatic document classification due to its various applications in data mining and information technology is one of the important topics in computer science. Classification plays a vital role in many information management and retrieval tasks. Document classification, also known as document categorization, is the process of assigning a document to one or more predefined category labels. Classification...
Combing one-versus-one decomposition strategy with support vector machine has become an efficient means for multi-label classification problem. But how to speed up its training and test procedures is still a challenging issue. In this paper, we generalize the primary binary support vector machine to construct a double label support vector machine through locating double label instances at marginal...
One of the main goals in machine learning is the general functional dependencies. Recent advances in kernel-based methods are focused on designing flexible and powerful input and output representations. This paper describes how rough set (RS) and support vector machine (SVM) can be practically implemented in ore-rock classification, and discusses the kernel mapping technique which is used to construct...
Improving the precision of shot boundary detection is very important. This paper presents an algorithm for shot boundary detection based on SVM (support vector machine) in compressed domain. It uses the features, such as the type of macroblock, the difference between DC coefficients of two co-located blocks in successive frames and the type of frame, to segment a video into the shots by classifying...
In this paper, a multi-steps algorithm based on support vectors machines (SVMs) in similarity space is proposed. The SVMs is used as a recent classification method and separation boundary estimation technique for high dimensional data. It benefits of limited number of data for training of supervised classification, which is a key challenge in hyperspectral data analysis. SVMs based classifier is applied...
Zadeh proposed that there are three basic concepts that underlie human cognition: granulation, organization and causation and a granule being a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality. In this paper, we give out a novel definition of Granular Computing which can be easily treated by neural network. Perception learning as granular computing...
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