PSVM: Parallelizing Support Vector Machines on Distributed Computers

Zhu, Kaihua; Wang, Hao; Bai, Hongjie; Li, Jian; Qiu, Zhihuan; Hang, C.C.; Chang, Edward Yi

doi:10.1007/978-3-642-20429-6_10

Cited by 128 publications

(87 citation statements)

References 9 publications

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“…First of all, the number of training examples was large enough to create a problem for our computational resources. The scaling of SVM to large data sets is indeed an active research area [2,7,18,19]. We turned our attention to a simple approach proposed by V.Vapnik et al in [11], called Cascade SVM.…”

Section: Tablementioning

confidence: 99%

Conformal prediction of biological activity of chemical compounds

Toccaceli

Nouretdinov

Gammerman

2017

Ann Math Artif Intell

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The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. The paper addresses some specific challenges in this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. This approach allowed us to identify the most likely active compounds for a given biological target and present them in a ranking order.

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Section: Tablementioning

confidence: 99%

Conformal prediction of biological activity of chemical compounds

Toccaceli

Nouretdinov

Gammerman

2017

Ann Math Artif Intell

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“…Chang et al [23] improved the scalability of SVMs using a parallel SVM algorithm (PSVM), which reduces memory use by performing a row-based, approximate matrix factorization that loads only essential data to each machine to perform parallel computation. PSVM reduces the memory requirement from O(n 2 ) to O(np/m) and improves the computation time to O(np 2 /m), where n denote the number of training instances, p the reduced matrix dimension after factorization ( p is significantly smaller than n), and m the number of machines.…”

Section: Parallelizationmentioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

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The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

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“…Very high efficiency and competitive accuracy have been achieved by the parallel implementation. Psvm proposed in [6] is based on interior point solver. It approximates the kernel matrix by incomplete Cholesky factorization.…”

Section: Related Workmentioning

confidence: 99%

“…The training of SVM is essentially a quadratic optimization problem which is both time and memory costly while running on computers, making it a challenge to apply SVM on large scale problems. Several optimizing or heuristic methods have been proposed to accelerate the training and reduce the memory occupation, such as shrinking, chunking [4], kernel caching [5], approximation of kernel matrix [6]. In addition, certain scalable solvers can be used such as Sequential Minimal Optimization (SMO) [7], mixture SVMs [8], primal estimated sub-gradient solver [9].…”

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confidence: 99%

Parallel Support Vector Machines on Multi-Core and Multiprocessor Systems

Zhao¹,

Magoulès²

2011

Artificial Intelligence and Applications / 718: Modelling, Identification, and Control

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Abstract-This paper proposes a new and efficient parallel implementation of support vector machines based on decomposition method for handling large scale datasets. The parallelizing is performed on the most time-and-memory consuming work of training, i.e., to update the vector f . The inner problems are dealt by sequential minimal optimization solver. Since the underlying parallelism is realized by the shared memory version of Map-Reduce paradigm, our system is easy to build and particularly suitable to apply to multi-core and multiprocessor systems. Experimental results show that on most of the tested datasets, our system offers higher than fourfold increase in speed compared to Libsvm, and it is also far more efficient than the MPI implementation Pisvm.

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PSVM: Parallelizing Support Vector Machines on Distributed Computers

Cited by 128 publications

References 9 publications

Conformal prediction of biological activity of chemical compounds

Conformal prediction of biological activity of chemical compounds

Scaling up data mining algorithms: review and taxonomy

Parallel Support Vector Machines on Multi-Core and Multiprocessor Systems

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