Kernel-based SVM Classifier Research Articles

Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing

Subspace analysis is an effective way for Structural Health Monitoring (SHM). In current research, linear algorithms for single-subspace analysis are commonly utilized. Nonlinearity of the structure and characteristics of subspace distribution are ignored. To overcome these shortcomings, characteristics of subspace set are analyzed and a nonlinear subspace distance is defined for SHM in this paper. To calculate this distance index, vibration response signals are firstly monitored and system subspaces are extracted by subspace analysis method. Then, subspace set is viewed as a Grassmann manifold, and the manifold is modeled by Grassmann kernel-based SVM classifier to describe its nonlinear characteristics. Finally, margin in SVM classifier modeled from Grassmann manifolds corresponding to structural normal state and abnormal state, respectively, is defined as a SHM index. This index indicates the degree of the abnormal state deviating from the normal state, and it is an effective index to reflect structural states. Effectiveness of the SHM index is validated by testing data of a Viscoelastic Sandwich Structure (VSS) with viscoelastic sandwich subjected to accelerated ageing in a thermal-oxygen ambient. Analysis result shows that the designed index is very effective to indicate health state in the VSS.

Sleep apnoea detection from ECG using features extracted from reconstructed phase space and frequency domain

Sleep apnoea is a very common sleep disorder which is able to cause symptoms such as daytime sleepiness, irritability and poor concentration. This paper presents a combinational feature extraction approach based on some nonlinear features extracted from Electro Cardio Graph (ECG) Reconstructed Phase Space (RPS) and usually used frequency domain features for detection of sleep apnoea. Here 6 nonlinear features extracted from ECG RPS are combined with 3 frequency based features to reconstruct final feature set. The nonlinear features consist of Detrended Fluctuation Analysis (DFA), Correlation Dimensions (CD), 3 Large Lyapunov Exponents (LLEs) and Spectral Entropy (SE). The final proposed feature set show about 94.8% accuracy over the Physionet sleep apnoea dataset using a kernel based SVM classifier. This research also proves that using non-linear analysis to detect sleep apnoea can potentially improve the classification accuracy of apnoea detection system.

Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)

Neural Networks and SVM are recently being used in various kind of pattern recognition. As humans, it is easy to recognize numbers, letters, voices, and objects, to name a few. However, making a machine solve these types of problems is a very difficult task .Character Recognition has been an active area of research in the field of image processing and pattern recognition and due to its diverse applicable environment, it continues to be a challenging research topic. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognized handwritten character using the multi layer feed forward back propagation neural network without feature extraction and SVM classifier. Character data is used for training the neural network and SVM. The trained network is used for classification and recognition. For the neural network, each character is resized into 70x50 pixels, which is directly subjected to training. That is, each resized character has 3500 pixels and these pixels are taken as features for training the neural network. For the SVM classifier recognition model is divided in two phases namely, training and testing phase. In the training phase 25 features are extracted from each character and these features are used to train the SVM. In the testing phase SVM classifier is used to recognize the characters. The results show that by applying the proposed system, we reached a high accuracy for the problem of handwritten character recognition.

Medical Image Categorization and Retrieval System in Radiology Using Bag of Visual Words Framework

In This work we present an efficient image categorization and retrieval system applied to Image Clef 2009 medical image retrieval task. In this task we have presented methodology is based on local patch representation of the image content and a bag-of-features approach for defining image categories, with a kernel based SVM classifier. Two main tasks are addressed: First organ identification task; second the detection and identification of pathologies, i.e. shifting from the organ level to pathology level analysis. We used a large generic archive of 12,000 radiographs (IRMA) to tune the system parameters. We demonstrate automated organ detection on the IRMA collection as well as the generalization to a new data collection. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. We proposed system was helped to find discriminating orientation and body regions in X-ray images also organ-level discrimination we show an application to pathology level categorization of chest X-ray data. Results indicate detection of pathology at a sensitivity of 88.4% and a specificity of 81%. This is first step towards similarity-based medical image categorization that has a major clinical importance in computer-assisted diagnostics. It can identify suspicious pathological X-rays and alert the referring clinicians to potential emergencies. Overall it is hoped that the development of such systems will contribute to the improvement of safety and quality of medical services

English

There are two paradigms for modelling the varying length temporal data namely, modelling the sequences of feature vectors as in the hidden Markov model-based approaches for speech recognition and modelling the sets of feature vectors as in the Gaussian mixture model (GMM)-based approaches for speech emotion recognition. In this paper, the methods using discrete hidden Markov models (DHMMs) in the kernel feature space and string kernel-based SVM classifier for classification of discretised representation of sequence of feature vectors obtained by clustering and vector quantisation in the kernel feature space are presented. The authors then present continuous density hidden Markov models (CDHMMs) in the explicit kernel feature space that use the continuous valued representation of features extracted from the temporal data. The methods for temporal pattern classification by mapping a varying length sequential pattern to a fixed-length sequential pattern and then using an SVM-based classifier for classification are also presented. The task of recognition of spoken letters in E-set, it is possible to build models that use a discretised representation and string kernel SVM based classification and obtain a classification performance better than that of models using the continuous valued representation is demonstrated. For modelling sets of vectors-based representation of temporal data, two approaches in a hybrid framework namely, the score vector-based approach and the segment modelling based approach are presented. In both approaches, a generative model-based method is used to obtain a fixed length pattern representation for a varying length temporal data and then a discriminative model is used for classification. These two approaches are studied for speech emotion recognition task. The segment modelling based approach gives a better performance than the score vector-based approach and the GMM-based classifiers for speech emotion recognition.Defence Science Journal, 2010, 60(4), pp.348-363, DOI:http://dx.doi.org/10.14429/dsj.60.492