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Reconstruction problem for signals generated by discrete nonlinear dynamic system is considered via unified approach to recurrent kernel-based dynamic systems. In order to prevent the model complexity increasing under on-line identification, the reduced order model kernel method is proposed and proper recurrent Least-Square identification algorithms are designed along with conventional regularization...
We propose an autism spectrum disorder (ASD) prediction system based on machine learning techniques. Our work features the novel development and application of machine learning methods over traditional ASD evaluation protocols. Specifically, we are interested in discovering the latent patterns that possibly indicate the symptom of ASD underneath the observations of eye movement. A group of subjects...
Today, Peer-to-peer (P2P) traffic is the most important network flow on the Internet; meanwhile it gives rise to many security problems for the network management. Therefore P2P traffic identification is the hottest topic of P2P traffic management. Support vector machine (SVM) has advantages with resolving small samples for P2P classification problems. However, the performance of SVM is primarily...
Texture is the vital feature for remote sensing image classification, however, it is hard to be described and recognized by computer vision. As a result, lots of approaches have been presented to identify texture image. Among these methods, support vector machine (SVM) is the most successfully used one, which takes advantages of avoiding local optimum, conquering dimension disaster with small samples...
Multiclass classification is the task of classifying the samples into more than two classes. Generally multi-classifiers face difficulty in classifying samples those are very close to the separating hyperplane, known as Generalization error. Generalization error can be reduced by maximizing the margin of the separating hyperplanes. Support Vector Machine (SVM) is a maximum-margin classifier, its aim...
Least squares support vector machine (LS-SVM) has been successfully applied in many classification and regression tasks. The main drawback of the LS-SVM algorithm is the lack of sparseness. Combing the primal least squares twin support vector machine (LS-TSVM) and the sparse LS-SVM with L0-norm minimization, a new sparse least squares support vector regression algorithm with L0-norm in primal space(L...
An object recognition method based on Gabor wavelet and SVM is proposed in this paper. First features of the object are extracted by using Gabor wavelet, and then the dimensions of the Gabor features are reduced with Principal Component Analysis, and finally classification is performed with Support Vector Machine. And this method is applied to the Columbia image library COIL-20 for experiments. Compared...
Along the prompt growth in World Wide Web, the availability and accessibility of regional language contents such as e-books, web pages, e-mails, and digital repositories has grown exponentially. As a result, the automatic document classification has become the hotspot for fetching information among the millions of web documents. The idea of classifying the text, forms the baseline for many NLP applications...
Micro-hand is one of soft actuators. It has many merits, however, it is of nonlinearity. Moreover, it needs sensorless control. Sensorless control with SVR-based mapping of a micro-hand is proposed in this paper. Effectiveness of the proposed method is verified by the experiment of the control system.
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...
Acoustic scene classification is a difficult problem mostly due to the high density of events concurrently occurring in audio scenes. In order to capture the occurrences of these events we propose to use the Subband Power Distribution (SPD) as a feature. We extract it by computing the histogram of amplitude values in each frequency band of a spectrogram image. The SPD allows us to model the density...
Recently, a customer review of travel information website has been a big influence on users in accommodations by the spread of the internet. In our research, as preprocessing of picking out information from the reviews, we propose a method to classify sentences into "Opinion sentences" and "Fact sentences" using SVM. And we confirm effectiveness of "opinion sentences"...
This article builds the system of comprehensive evaluation index system for transformer substation address selection with the analysis of Huadian project for power transmission and transformation, due to the comprehensive evaluation of power transmission and transformation project involving multiple factors, analytic hierarchy process (AHP) is used to determine the weight of each evaluation index...
Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy,...
The Internet produces massive financial unstructured textual information every day. How to utilize these unstructured data effectively is a challenging topic. In the background of A share T+0 and stock option promoting in the China security market, we present a model to recognize the risk and investment opportunity according to the massive online financial textual information. Since the key word vector...
This paper presents a set of improvements for SVM-based large scale multimedia indexing. The proposed method is particularly suited for the detection of many target concepts at once and for highly imbalanced classes (very infrequent concepts). The method is based on the use of multiple SVMs (MSVM) for dealing with the class imbalance and on some adaptations of this approach in order to allow for an...
Support vector machine (SVM) is a popular classifier dealing with small-scale datasets. It has outstanding performance compared to other classifiers. However the execution time is extremely long when training Big Data. The Graphics Processing Unit (GPU) is a massively parallel device which performs very well as a co-processor. NVIDIA proposed a programming platform, CUDA, in 2006, which makes it much...
Diagnosis of disease is done by physical examination of patient by physician. For internal observation physician requires help of sonography, MRI, pathological tests reports etc. In Ayurveda Nadi-Pariksha (pulse examination) is used for making the diagnosis. It uses pulse signal sensed at radial artery on wrist below the thumb for diagnosis manually. The pulse signal contains very useful information...
We present a method to segment kidneys in 3D ultrasound images. The main challenges are the high variability in kidney appearance, the frequent presence of artifacts (shadows, speckle noise, etc.) and a strong constraint on computation time for clinical acceptance (less than 10 seconds). Our algorithm leverages a database of 480 3D images through a support vector machine(SVM)-based detection algorithm...
Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
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