HELAD: A Novel Network Anomaly Detection Model Based on Heterogeneous Ensemble Learning

The immense amount of data created by digitalization requires parallel computing for machine-learning methods. While there are many parallel implementations for support vector machines (SVMs), there is no clear suggestion for every application scenario. Many factor—including optimization algorithm, problem size and dimension, kernel function, parallel programming stack, and hardware architecture—impact the efficiency of implementations. It is up to the user to balance trade-offs, particularly between computation time and classification accuracy. In this survey, we review the state-of-the-art implementations of SVMs, their pros and cons, and suggest possible avenues for future research.

Parallel Computing of Support Vector Machines: A Survey

With rapid growth of social networking service on Internet, huge amount of information are continuously generated in real time. As a result, sentiment analysis of online reviews and messages has become a popular research issue [1]. In this paper a novel modified Chi Square-based feature clustering and weighting scheme is proposed for the sentiment analysis of twitter message. Along with the part of speech tagging, the discriminability and dependency of the words in the tagged training dataset are taken into account in the clustering and weighting process. The multinomial Naive Bayes model is also employed to handle redundant features, and the influence of emotional words is raised for maximizing the accuracy. Computer simulation with Sentiment 140 workload shows that the proposed scheme significantly outperforms four existing representative sentiment analysis schemes in terms of the accuracy regardless of the size of training and test data.

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Word clustering based on POS feature for efficient twitter sentiment analysis

Ligand based virtual screening using SVM on GPU.

This paper addresses the problem of real-time model predictive control (MPC) in the integrated guidance and control (IGC) of missile systems. When the primal-dual interior point method (PD-IPM), which is a convex optimization method, is used as an optimization solution for the MPC, the real-time performance of PD-IPM degenerates due to the elevated computation time in checking the Karush–Kuhn–Tucker (KKT) conditions in PD-IPM. This paper proposes a graphics processing unit (GPU)-based method to parallelize and accelerate PD-IPM for real-time MPC. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional PD-IPM and other methods showed that the proposed method improved the real-time performance by reducing the computation time significantly.

https://www.mdpi.com/1424-8220/22/12/4512/pdf?version=1655267674

GPU-Accelerated PD-IPM for Real-Time Model Predictive Control in Integrated Missile Guidance and Control Systems

Linear rankSVM is one of the widely used methods for learning to rank The existing methods such as trust region Newton method (TRON) with order-statistic tree cannot perform efficiently when any one of them is exploited to deal with the large-scale data sets It is observed that training the linear rankSVM with L2-loss requires a large amount of data-intensive computing, and therefore is feasible to make use of the great computational power of GPU to improve the training speed In this paper, we propose two efficient parallel algorithms (named PSWX-TRON and PSY-TRON) to train the linear rankSVM with L2-loss on the GPU architecture The proposed parallel algorithms can work efficiently on GPU if the training set is divided in terms of different queries The experimental results show that compared with the existing methods, the proposed algorithms not only can obtain the impressive training speeds, but also can perform well in prediction

GPU-accelerated parallel algorithms for linear rankSVM

The training of SVM can be viewed as a Convex Quadratic Programming (CQP) problem which becomes difficult to be solved when dealing with the large scale data sets. Traditional methods such as Sequential Minimal Optimization (SMO) for SVM training is used to solve a sequence of small scale sub-problems, which costs a large amount of computation time and is hard to be accelerated by utilizing the computation power of GPU. Although Interior Point Method (IPM) such as primal-dual interior point method (PDIPM) can be also addressed SVM training well and has favourable potential for parallelizing on GPU, it contains comparatively high time complexity O(l^3) and space complexity O(l^2), where l is the number of training instances. Fortunately, by invoking low-rank approximation methods such as Incomplete Cholesky Factorization (ICF) and Sherman Morrison Woodbury formula (SMW), the requirements of both storage and computation of PDIPM can be reduced significantly. In this paper, a parallel PDIPM method (P-PDIPM) along with a parallel ICF method (P-ICF) is proposed to accelerate the SVM training on GPU. Experimental results indicate that the training speed of P-PDIPM on GPU is almost 40x faster than that of the serial one (S-PDIPM) on CPU. Besides, without extensive optimization, P-PDIPM can obtain about 8x speedup over the state of the art tool LIBSVM while maintaining high prediction accuracy.

Efficient SVM Training Using Parallel Primal-Dual Interior Point Method on GPU

Linear RankSVM is one of the widely used methods for learning to rank The existing methods, such as Trust-Region Newton (TRON) method along with Order-Statistic Tree (OST), can be applied to train the linear RankSVM effectively However, extremely lengthy training time is unacceptable when using any existing method to handle the large-scale linear RankSVM To solve this problem, we thus focus on designing an efficient distributed method (named DLRankSVM) to train the huge-scale linear RankSVM on distributed systems First, to efficiently reduce the communication overheads, we divide the training problem into subproblems in terms of different queries Second, we propose an efficient heuristic algorithm to address the load balancing issue (which is a NP-complete problem) Third, using OST, we propose an efficient parallel algorithm (named PAV) to compute auxiliary variables at each computational node of the distributed system Finally, based on PAV and the proposed heuristic algorithm, we develop DLRankSVM under the framework of TRON The extensive empirical evaluations show that DLRankSVM not only can obtain impressive speedups on both multi-core and distributed systems, but also can perform well in prediction compared with the other state-of-the-art methods To the best of our knowledge, this is the first research work proposed to train the huge-scale linear RankSVM in a distributed fashion

Jing Jin

Papers

GPU-accelerated parallel algorithms for linear rankSVM

Efficient SVM Training Using Parallel Primal-Dual Interior Point Method on GPU

DLRankSVM: an efficient distributed algorithm for linear RankSVM