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Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
Risk permeates all aspects of doing business. However, support tools capable of systematically identifying the complete spectrum of risks that a company might face are currently lacking. Such a tool would need to reliably identify company-risk relationships from unstructured sources, therefore providing a qualitative assessment of risk exposure. We propose a supervised learning approach that combines...
This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.
The brain is one of the vital organ of the body where it is the custodian of the involuntary and voluntary actions like walking, vision, memory. Now a days the most common brain disorders are Alzheimer's disease, Epilepsy (paralysis or stroke), tumors, brain tumors. Early diagnosis and proper treatment of brain tumors is required. The Computer Aided Diagnostic tools (CAD) can be used by the doctor...
The objective of this study is to create a forecast model for the buying and selling points of stocks, using a support vector machine (SVM) model in order to create a highly accurate prediction. The trials compare four Kernel functions of SVM, consisting of Dot function, Radial Basis Function (RBF), Sigmoid function, and Polynomial function to evaluate which Kernel would provide the most accurate...
The Support Vector Machine (SVM) is a classical classification algorithm that has a wide range of application. With kernel function, SVM can dispose the datasets that are not linearly separable in their original feature space, making it more flexible in practical use compared with linear model. However, its complexity in training is an obstacle to large-scale dataset handling. This paper proposes...
The Performance of three classification methods; Support Vector Machine (SVM), Naive Bayes (NB) and k-Nearest Neighbor (k-NN) is investigated by a detailed comparison for detection of line outage. This paper presents an application of Phasor Measurement Units (PMUs) in the protection of electrical power systems that is the single line outage detection in a transmission network. Also, a detection of...
Prediction for deck-motion is a practical measure to improve the landing/taking off safety of carrier-based aircraft when those deck-motions in six-degree freedoms cannot be effectively controlled/restrained. Deck-motions excited by waves and winds own characteristics of randomness and nonlinearity. It is generally believed those classical feed-forward neural networks, such as back propagation networks...
In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis...
This paper studies on the Day-of-the-week effect by means of several binary classification algorithms in order to achieve the most effective and efficient decision trading support system. This approach utilizes the intelligent data-driven model to predict the influence of calendar anomalies and develop profitable investment strategy. Advanced technology, such as time-series feature extraction, machine...
The paper presents a unique combination of texture feature extraction techniques which can be used in image texture analysis. Setting the prime objective of classifying different texture images, the Local Binary Pattern (LBP) and a modified form of Gray Level Run Length Matrix (GLRLM) are implemented initially. The next phase involves use of combination of the former two methods to extract improved...
With the development of widely-used unmanned aerial vehicles (UAV), automatic object recognition for UAV aerial images has important practical values. Since the background of objects is complex, there are limitations in object recognition using single-source visible or infrared data. Multi-source images contain much more information of objects, which can improve the recognition rate. Meanwhile there...
Ground Penetrating Radar (GPR) is used for subsurface exploration across different applications like landmines detection. It can detect and deliver the response of any buried kinds of object, however it cannot discriminate between landmines and false alarms. In this paper, we propose a detection method based on support vector machine (SVM) using one-dimensional GPR delivered data called Ascans. Each...
Image classification is one the important processing done on satellite images. Many algorithm are proposed for such classification of which Support Vector Machine (SVM) is mostly used. Many variants and approaches of SVM are proposed of which GA based classifiers shows better prospects. But increasing size, spectrum and multiple dimension of remote sensing data has made image processing problem more...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
Blind steganalysis is a method used to detect whether there is a hidden message in a media without having to know the steganography algorithm behind it. Digital image is converted into features using feature extraction algorithm subtractive pixel adjacency matrix. A model is built based on the resulting features using machine learning method support vector machine. The support vector machine method...
Active learning (AL) methods that select unlabeled samples only querying by informative measures (i.e., uncertainty and/or diversity criteria) have been extensively investigated. However, these methods usually do not exploit the manifold structure of the unlabeled data from the geometrical point of view, a choice that might lead to a sample bias and consequently undesirable performances. To control...
Feature selection and parameters optimization is an important step in the using of SVM. In recent years, more researchers are mainly focus in feature selection and parameters optimization. However, the number of support vectors with the selected support vector subset also has an effect on classification performance of SVM. Few researchers concentrate on this area. This paper proposed a novel optimization...
This paper proposes a new Utterance Verification (UV) algorithm based on i-vector. Phone segments are extracted and concatenated from the training data, which are used to train the Universal Background Model (UBM) and the Total Variability (TV) matrix, and then, i-vector is extracted from the enrollment and evaluation data using UBM and TV matrix. We compare two Confidence Measures (CMs), cosine distance...
Hypertension poses a serious atherosclerotic risk as it causes both macro- and micro circulation damage. Nailfold capillaroscopy is a valuable yet simple tool to assess microcirculation of blood capillaries. This technique is important in detecting early occurrences of scleroderma spectrum disorders and evaluating Raynaud's Phenomenon. Here it is used in detecting hypertension in patients. Current...
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