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The error-correcting output codes (ECOC) method reduces the multi-class learning problem into a series of binary classifiers. In this paper, we propose a modified Hadamard-type ECOC method. This method uses both N'th order and N/2'th-order Hadamard matrix to construct error correcting output codes, which is called hybrid Hadamard ECOC. Experiments based on dichotomizers of support vector machines...
This paper describes a system for detection and classification of moving objects based on support vector machines (SVM) and using 3D data. Two kinds of camera systems are used to provide the classification system with 3D range images: time-of-flight (TOF) camera and stereo vision system. While the former uses the modulated infrared lighting source to provide the range information in each pixel of...
This paper deals with new regression method of predicting fuzzy multivariable nonlinear regression models using triangular fuzzy numbers. The proposed method is achieved by implementing the locally weighted least squares support vector machine regression where the local weight is obtained from the positive distance metric between the test data and the training data. Two types of distance metrics for...
In this paper, we discuss a problem of multi-classification on temporal data. First, we present a classification model. According to the characteristic of temporal data and the need of the fact, we propose a weighted support vector multi-classification algorithm and analyze its classification performance theoretically. This algorithm introduces weight factors for samples and classes. Finally, experiments...
The conventional pairwise classification shows superior performances for those classifiable samples, but unclassifiable regions exist. DDAG based SVMs can resolve unclassified regions, but it only employ one decision function at each step, and never considers all decision function together, which may hurt its classification performance. In this paper we propose two-stage SVMs to combine their merits...
The paper attempts to introduce a fundamental fuzzy concept to break the equivalent attitude of the input training set of SVM, and tries to give individual example in the set a different attitude. The attitude can stand for the influence that the example takes into account in the classification. In the paper, we present a method to refresh the attitude by assigning proper fuzzy value to the class...
Fingerprint identification is an important biometric technique which has been used and studied a lot in the past. The most popular traditional approach for identifying fingerprint basing on extracting minutiae and then matching them together to authenticate has some limitations in practice. For a considerable fraction of population, it is very difficult to automatically extract minutiae in a noise...
The paper proposes a model merging a non-parametric k-nearest-neighbor (kNN) method into an underlying support vector machine (SVM) to produce an instance-dependent loss function. In this model, a filtering stage of the kNN searching was employed to collect information from training examples and produced a set of emphasized weights which can be distributed to every example by a class of real-valued...
This paper is concerned with the fuzzy support vector classification in which the type of both the output of the training point and the value of the final fuzzy classification function is triangle fuzzy number. First, the fuzzy classification problem is formulated as a fuzzy chance constrained programming. Then we transform this programming into its equivalence quadratic programming. As a result,...
In fault pattern recognition field, the real-time online fault diagnosis is a new requirement especially from the high-speed machines, and also the magnificent researching direction. The precision and speed of the classification are important research issues in fault pattern recognition for this kind of intelligent fault diagnosis. Although many improved ANN (artificial neural net) methods have been...
Gene expression data that is being used to gather information from tissue samples is expected to significantly improve the development of efficient tumor diagnosis. For more accurate classification of tumor, extracting discriminant components from thousands of genes is an important problem which becomes challenging task due to the large number of genes and small sample size. We propose a novel approach...
Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon rank sum test and support vector machine (SVM) is proposed in this paper. First, Wilcoxon rank sum test is used to select a subset. Then each selected gene is trained and tested using SVM classifier with linear kernel separately, and genes with high testing accuracy rates are chosen...
This article presents a masquerade detection system based on correlation eigen matrix and support vector machine (SVM). The system first creates a profile defining a normal user's behavior by correlation eigen matrix, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is valid user or masquerader. In order to avoid overfitting and reduce...
Target recognition based on high range resolution (HRR) polarized radar using support vector machines (SVMs) was studied in this paper. A fuzzy membership function was constructed based on SVM decision-making function in order to improve the performance of OAA and OAO classifiers for multi-class target, and HRR radar target recognition method using improved SVM was proposed: First, the polarized radar...
This paper presents a method of Chinese text chunking based on editing support vector machine (ESVM) and K nearest neighbors (KNN). The word itself, part-of-speech (POS) tag, syllable and context information is extracted as the features of the vectors. The experimental results show that this model is efficient for Chinese text chunking. The hybrid machine learning model based on ESVM and KNN can achieve...
This paper addresses the problem of face detection in color image. In this paper, a coarse-to-fine approach to face detection is presented. Firstly, a novel self-skin segmentation algorithm is proposed for skin detection, instead of conventional skin color model. In the second step, edge information and mathematical morphology method are integrated to progressively restrict the possible candidate...
The paper uses the learning algorithm of support vector machine to separate both 106 listed companies of China in 2000 and 80 borrowers of a national commercial bank of China in 2001 into two patterns respectively by using two different kernel functions: polynomial function and radial basis function. The experimental results show that, under the circumstance of LIBSVM, the learning algorithms of support...
Electricity consumption reflects the electricity usage of the whole society, so the prediction study and analysis to electricity consumption have important realistic and theoretical significance. The influence that different factors affect the electricity consumption is in different degrees. And the influence works in complicated ways, which makes the characteristics of the electricity consumption...
Wavelet packet theory and support vector regression (SVR) were introduced into server load prediction. A novel prediction algorithm called wavelet packet-SVR was proposed. Firstly, the algorithm decomposed and reconstructed the load time series into several signal branches by wavelet packet analysis. Secondly, SVR prediction models were constructed respectively to these branches and finally their...
Spam mail is bombing and causing a great problem, while there is still no appropriate counter-measures. After discussing some spam-filtering systems, this paper brings forward a spam-filtering system based on feature selection mechanism and improved SVM classification, system design, realization and evaluation is discussed following. Performance measure and experimental results are proved to outperform...
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