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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...
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 machine (SVM) has been shown powerful in binary classification problems. In order to accommodate SVM to speaker verification problem, the concept of sequence kernel has been developed, which maps variable-length speech data into fixed-dimension vectors. However, constructing a suitable sequence kernel for speaker verification is still an issue. In this paper, we propose a new sequence...
The paper is related to the error analysis of Support Vector Machine (SVM) classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernels as the Mercer kernel and give the error estimate with De La Vallée Poussin means which improve the approximation error. On the other hand, the distortion is replaced by the uniformly boundedness of the Cesàro means. We also introduce...
The maintenance period, the time frame begging from the registration to the expiration of a patent, is an important property used to evaluate the patent's quality. To predict the maintenance period of a patent, a consistent classifier is desirable. The output of a classifier is usually determined by the value of a discriminant function and a decision is made based on this output which does not necessarily...
Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of...
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...
Support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method...
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...
In a SVM classifier, the training speed is sensitive to the quantity of dataset. Therefore, the methodology of choosing some useful data that can decrease the number of training data and accelerate the training speed is usually a topic to be discussed on the SVM data process. The hyperplane of SVM is constructed by a small number of vectors. These vectors, whose locations are distributed in other...
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...
In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis....
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...
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...
In this paper, we address the problem of combining linear support vector machines (SVMs) for classification of large-scale nonlinear datasets. The motivation is to exploit both the efficiency of linear SVMs (LSVMs) in learning and prediction and the power of nonlinear SVMs in classification. To this end, we develop a LSVM mixture model that exploits a divide-and-conquer strategy by partitioning the...
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...
Nowadays, information disclosure is a noticeable topic to both practice and academy since it has significant effect on corporate governance and capital market operation. Open and transparent information disclosure can reduce the information asymmetry between insiders and outsiders. The main purpose of this study is to construct an information transparency evaluation model. In this paper, we used the...
Multi-class approaches for SVMs are based on composition of binary SVM classifiers. Due to the numerous binary classifiers to be considered, for large training sets, this approach is known to be time expensive. In our approach, we improve time efficiency using concurrently two strategies: incremental training and reduction of trained binary SVMs. We present the exact migration conditions for the binary...
Over the last years significant effort has been made to improve the performance of speech recognition. The Fisher Kernel has been suggested as good ways to combine and underlying generative model in the feature space and discriminant classifiers such as SVMs. Chinese name speech patterns are difficult to be classified especially when they are similar in pronunciation. Continuous density hidden Markov...
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