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Network traffic classification plays a fundamental role in network management and services. Given error accumulation in traditional DAGSVM (Directed Acyclic Graph-Support Vector Machine) algorithm, we propose an improved DAGSVM classification method using two different possibility metrics in this paper. Differing from traditional DAG-SVM, the improved DAG-SVM algorithm eliminates one class only under...
To make the traditional support vector data description (SVDD) achieve better generalization performance and more robust against noise, a selective ensemble method based on correntropy and Renyi entropy is proposed. In this proposed ensemble method, the correntropy between the radii of the basis classifiers and the radius of the ensemble is utilized to substitute the sum-squared-error (SSE) criterion...
Support vector machine (SVM) is a machine learning method developed in the mid-1990s based on statistical learning theory. SVM classifier is currently more popular classifier. This paper presents a boundary detection technique for retaining the potential support vector. Through seeking to structural risk minimization of the SVM, it improves the learning generalization ability and achieves the minimization...
Image segmentation is an important step in many image processing techniques. In this paper, a new semi-supervised approach for color image segmentation is proposed. This method takes advantage of a limited human assistant. After an unsupervised segmentation stage, classes of some regions are questioned from the user. These user hints are used as an initial sample data and will be iteratively expanded...
Active learning has exhibited strong ability in improving efficiency of practical classification tasks. However, when applied to imbalanced datasets, traditional selecting strategies for active learning can be severely disturbed by redundant majority instances thus fail to offer inherent capability. In this paper, Fisher Information Matrix based pair wise selection (FIMPS) is proposed to solve the...
The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular,...
Among the various classifiers, the Support Vector Data Description (SVDD) is a well-known strong classifier since it uses nonparametric boundary approach that constructs the minimum hypersphere enclosing the target objects as much as possible. The SVDD has been used in many studies for classification, anomaly and target detection problems on airborne or spaceborne remote sensing hyperspectral images...
In this paper, the use of clustering algorithms for decision level data fusion is proposed. Person authentication results coming from several modalities (e.g. still image, speech), are combined by using the fuzzy k-means (FKM) and the fuzzy vector quantization (FVQ) algorithms, two modification of them that use fuzzy data FKMfd and FVQfd, and a median radial basis function (MRBF) network. The modifications...
The three-class recognition problem of respiratory sounds based on multi-stage decisions is addressed. The method consists of dividing respiratory cycles of patients into phases, and classifying each phase with a separate multilayer perceptron, called the “phase expert”. Each phase information consists of several time segments and their parametric representation. Expert decisions on phase segments...
In this paper, a new initialization method is developed for enhancing the LBG codebook design algorithm in image vector quantization. The proposed method first arranges the training set data according to three different characteristics of the training vector, i.e. mean, variance and shape. A sampling method based on the criterion of maximum error reduction is then developed to select the desired number...
In multi-class problems, within- and between-class scatters should be considered in classification criterion. The common vector approach (CVA) uses the discriminative information obtained from within-class scatter of any class. It has been shown that this classical CVA method gives high recognition rates in multi-class problems. In this study, improvements on the CVA method that consider both within-...
This paper introduces an audio watermark (WM) decoding scheme that performs a Support Vector Machine (SVM) based supervised learning followed by a blind decoding. The decoding process is modelled as a two-class classification procedure. Initially, wavelet decomposition is performed on the training audio signals, and the decomposed audio frames watermarked with +1 and −1 constitute the training sets...
A typical concept-detection problem is characterised by greatly disproportionate sizes of the populations of training samples in the concept and anti-concept classes. In many cases, the population of anti-concept (negative) examples outnumber the concept examples. In this paper, an inverse random under sampling method is proposed to solve this imbalance problem. By the proposed method of inverse under...
This work compares two classification techniques used in audio indexing tasks: Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). GMM is a classical technique taken as reference for comparing the performance of SVM in terms of accuracy and execution time. For testing the methodologies, we perform speech and music discrimination in radio programs and environment sounds (laughter and applause)...
The Common Vector (CV) method is a linear method, which allows to discriminate between classes of data sets, such as those arising in image and word recognition. In this paper a variation of this method is introduced for finding the projection vectors of each class as elements of the intersection of the null space of that class' covariance matrix and the range space of the covariance matrix of the...
This paper studies a new method for identifying the new words, Objective to identify new words better. Method is first to extract the positive and negative samples from training corpus which was handled by segmentation and POS Tagging according to the dictionary, then combining with all kinds of words classification which was gotten from training corpus, and gaining the new word support vector through...
The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
The use of feature vectors obtained by concatenation of different features for text independent speaker identification from clean and telephone speech is studied. The composite feature vectors are examined with GMM and VQ models used to classify speakers. Linear discriminant analysis (LDA), a statistical tool designed to select a reduced set of features for best classification, is applied to enhance...
Mining opinions and analyzing sentiments from social network data help in various fields such as even prediction, analyzing overall mood of public on a particular social issue and so on. This paper involves analyzing the mood of the society on a particular news from Twitter posts. The key idea of the paper is to increase the accuracy of classification by including Natural Language Processing Techniques...
Embedded sensing systems conventionally perform A-to-D conversion followed by signal analysis. In many applications, the analysis of interest is inference (e.g., classification), but the sensor signals involved are too complex to model analytically. Machine learning is gaining prominence because it enables data-driven training of classifiers, overcoming the need for analytical models. This work presents:...
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