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In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse auto encoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse auto encoder to learn the features of the data. Secondly,...
Machine learning techniques were applied to job accounting and performance data for application classification. Job data were accumulated using the XDMoD monitoring technology named SUPReMM, they consist of job accounting information, application information from Lariat/XALT, and job performance data from TACC_Stats. The results clearly demonstrate that community applications have characteristic signatures...
The lack of information of complicated industrial systems represents one of the main limitation to implement condition monitoring and diagnosis systems. Novelty detection framework plays an essential role for monitoring systems in which the information about the different operation conditions or fault scenarios is unavailable or limited. In this context, this work presents a novelty detection approach...
We consider automatic performance tuning of stencil computations on Graphics Processing Units. We present a strategy that uses machine learning to determine the best way to use memory followed by a heuristic that divides the remaining optimizations into groups and exhaustively explores one group at a time. We evaluate our strategy using 102 synthetically generated OpenCL stencil kernels on an Nvidia...
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...
Recently, mainly due to the advances of deep learning, the performances in scene and object recognition have been progressing intensively. On the other hand, more subjective recognition tasks, such as emotion prediction, stagnate at moderate levels. In such context, is it possible to make affective computational models benefit from the breakthroughs in deep learning? This paper proposes to introduce...
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...
Fault detection plays an important role in advanced control of complex dynamic systems since precise information about system condition enables efficient control. Data driven methods of fault detection give the chance to monitor the plant state purely based on gathered measurements. However, they especially nonlinear, still suffer from a lack of efficient and effective learning methods. In this paper...
A Support Vector Machine (SVM) based approach for microgrid islanding decision and control is investigated. The IEEE 13-feeder system is modified and serves as the microgrid model connected to Kundur four-machine two-area system that models the main transmission grid. A representative data set is obtained through simulations in MATLAB/Simulink considering multiple typical scenarios with or without...
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,...
One of the many challenges for translating noninvasive glucose measurement into clinical practice is the calibration of the measuring instrument. In this work, least squares support vector regression (LS-SVR) has been used to develop a multivariate calibration model for determination of glucose concentration from near infra-red (NIR) spectra. The behaviour of developed model is studied on NIR spectra...
To alleviate the loads of tracking web log file by human effort, machine learning methods are now commonly used to analyze log data and to identify the pattern of malicious activities. Traditional kernel based techniques, like the neural network and the support vector machine (SVM), typically can deliver higher prediction accuracy. However, the user of a kernel based techniques normally cannot get...
To improve performance of large-scale scientific applications, scientists or tuning experts make various empirical attempts to change compiler options, program parameters or even the syntactic structure of programs. Those attempts followed by performance evaluation are repeated until satisfactory results are obtained. The task of performance tuning requires a great deal of time and effort. On account...
In the past years there are several machine learning techniques have been proposed to design precise classification systems for several medical issues. This paper compares and analyses breast cancer classifications with different machine learning algorithms using k-Fold Cross Validation (KCV) technique. Decision Tree, Naïve Bayes, Neural Network and Support Vector Machine algorithm with three different...
Heterogeneous computing, which combines devices with different architectures, is rising in popularity, and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programing such systems, and offers functional portability. It does, however, suffer from poor performance portability, code tuned for one device must be re-tuned to achieve good...
In this paper, the maximum entropy property of the discrete-time first-order stable spline kernel is studied. The advantages of studying this property in discrete-time domain instead of continuous-time domain are outlined. One of such advantages is that the differential entropy rate is well-defined for discrete-time stochastic processes. By formulating the maximum entropy problem for discrete-time...
With the pervasiveness of MapReduce - one of the most prominent programming models for data parallelism in Apache Hadoop-, many researchers and developers have spent tremendous effort attempting to boost the computational speed and energy efficiency of MapReduce-based big data processing. However, the scalable and fault-tolerant nature of MapReduce introduces additional costs in disk IO and data transfer,...
The machine learning is more effective today in anomaly detection to improve the classification accuracy. The use of powerful kernel based learning is very practical in current trends may expose accurate results in real time database applications. In this context, we need to use the new and adorned machine learning classifiers. In this paper we have given very successful and emerged kernels SVM (Support...
The Current description standards for Web Services such as WSDL and UDDI have a significant drawback of being restricted to the syntactic aspects of service. A service provider registers a service in the universal repository i.e. UDDI so that the service consumers can search and discover the required service that meets the user functional requirements from thousands of registered services. Matching...
Taking motivation from Twin Support Vector Machine (TWSVM), Peng (2009) attempted to propose Twin Support Vector Regression (TSVR) where regressor was obtained via solving pair of Quadratic Programming Problems(QPPs). However the discussed formulation was not on the lines of TWSVM and had some restrictions. In this paper we propose formulation termed as Twin Support Vector Machine based Regression(TWSVR)...
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