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Defect prediction models are classifiers that are trained to identify defect-prone software modules. Such classifiers have configurable parameters that control their characteristics (e.g., the number of trees in a random forest classifier). Recent studies show that these classifiers may underperform due to the use of suboptimal default parameter settings. However, it is impractical to assess all of...
According to the time and space, randomness and volatility of traffic flow, a short-term traffic flow forecasting model based on empirical mode decomposition (EMD), genetic particle swarm optimization(GPSO) and support vector machine (SVM) is proposed. Firstly, the traffic flow sequence is decomposed into different frequency components by EMD. Then the crossover and mutation factors of the genetic...
The SVM can realize data classification and prediction, the selection of penalty parameter c and kernel function g in training models directly affect the forecasting accuracy of the classification, the article use the K-CV method for c, g parameters optimization and processing, in wine species identification as an example to predict classification, improves the forecast accuracy, has reached the expected...
Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. If this is not possible, the data structure has to be inferred from the mere signal observations. This is exactly the problem that we address in...
Despite the importance of distributed learning, few fully distributed support vector machines exist. In this paper, not only do we provide a fully distributed nonlinear SVM; we propose the first distributed constrained-form SVM. In the fully distributed context, a dataset is distributed among networked agents that cannot divulge their data, let alone centralize the data, and can only communicate with...
This article proposes a performance analysis of kernel least squares support vector machines (LS-SVMs) based on a random matrix approach, in the regime where both the dimension of data p and their number n grow large at the same rate. Under a two-class Gaussian mixture model for the input data, we prove that the LS-SVM decision function is asymptotically normal with means and covariances shown to...
As a new emerging technology for wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge to deploy a separate receiver chain of front-end circuits in a dense circuit board. In this paper, we apply the compressive sensing technique to reduce the required number of front-end circuits and the overall computational complexity. Unlike the commonly adopted random...
In this paper, we address the problem of detecting and segmenting partial image blur from a single input image. Instead of assuming particular image priors or requiring additional user annotation, we propose a novel learning framework which jointly solves the tasks of blur kernel estimation and image blur segmentation, so that partial image blur can be automatically separated from the remaining parts...
In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures...
Binary descriptors not only are beneficial for similarity search, they are also capable of serving as discriminant features for classification purpose. In this paper we propose a new algorithm based on cross entropy to learn effective binary descriptors, dubbed CE-Bits, providing an alternative to L-2 and hinge loss learning. Because of the usage of cross entropy, a min-max binary NP-hard problem...
In the fields of remote sensing image processing, hyperspectral image (HSI) classification is a hot topic, which involves computer graphics, statistics, matrix theory and other disciplines. In this study, we prefer to use machine learning classification algorithm (supervised learning or unsupervised learning) to predict under the context of HSI classification. Recently, sparse representation based...
Previous works in the literature have shown the feasibility of general purpose computations for non-visual applications on low-end mobile graphics processors using graphics APIs. These works focused only on the functional aspects of the software, ignoring the implementation details and therefore their performance implications due to their particular micro-architecture. Since various steps in such...
Exascale computing is facing a gap between the ever increasing demand for application performance and the underlying chip technology that does no longer deliver the expected exponential increases in CPU performance. The industry is now progressively moving towards dedicated accelerators to deliver high performance and better energy efficiency. However, the question of programmability still remains...
Recently, total variation based image deconvolution has shown its superior performance. The restoration quality is generally sensitive to the value of regularization parameter. In this work, we develop a data-driven optimization scheme based on minimization of Stein's unbiased risk estimate (SURE)—statistically equivalent to mean squared error (MSE). Based on a typical alternating direction method...
Grid scheduling is the process of making scheduling decisions involving resources over many administrative domains. Resource selection is termed as resource discovery, assignment of application tasks to resources, and data staging. The Grid scheduler does not control the set of jobs submitted to it, or even know about jobs being sent to resources so decisions that trade-off one job's access for another's...
GEMM is the main computational kernel in BLAS3. Its micro-kernel is either hand-crafted in assembly code or generated from C code by general-purpose compilers (guided by architecture-specific directives or auto-tuning). Therefore, either performance or portability suffers. We present a POrtable Compiler Approach, Poca, implemented in LLVM, to automatically generate and optimize this micro-kernel in...
Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the...
This paper shows that many applications exhibit execution-phase-specific sensitivity towards approximation of the internal subcomputations. Therefore, approximation in certain phases can be more beneficial than others. Further, this paper presents Opprox, a novel system for application's execution-phase-aware approximation. For a user provided error budget and target input parameters, Opprox identifies...
We present an effective code compression technique to reduce the area and energy overhead of the configuration memory for coarse-grained reconfigurable architectures (CGRA). Based on a statistical analysis of existing code, the proposed method reorders the storage locations of the reconfigurable entities and splits the wide configuration memory into a number of partitions. Code compression is achieved...
This paper proposes the design of antipodal Vivaldi antennas using the kernel regression method. The kernel regression is applied for training a cost function model to predict the next sample with improved cost values, and the information of the predicted sample is employed to re-train the model. This process is repeated until the cost value converges to our design goal. The shapes of the tapered...
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