&lt;title&gt;Unsupervised optimization of support vector machine parameters&lt;/title&gt;

Cassabaum, Mary L.; Waagen, Donald; Rodríguez, Jeffrey J.; Schmitt, Harry A.

doi:10.1117/12.542422

Cited by 7 publications

(3 citation statements)

References 9 publications

(4 reference statements)

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“…8) must be optimally selected to obtain good estimation accuracy. In the literature there are many different approaches for selecting these parameters [47, [51][52][53]. In this work we have used the analytical methods proposed by Cherkassky and Ma [47].…”

Section: Parameter Selection For the Svm Forecasting Modelmentioning

confidence: 99%

Efficient resource provisioning for elastic Cloud services based on machine learning techniques

et al. 2019

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Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requirements in terms of latency or response time, such as web servers with high traffic load, data stream processing, or real-time big data analytics. Elasticity is often implemented in cloud platforms and virtualized data-centers by means of auto-scaling mechanisms. These make automated resource provisioning decisions based on the value of specific infrastructure and/or service performance metrics. This paper presents and evaluates a novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory. The new mechanism aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user, while attenuating resource over-provisioning in order to reduce energy consumption and infrastructure costs. The results show that the proposed model obtains a better forecasting accuracy than other classical models, and makes a resource allocation closer to the optimal case.

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Section: Parameter Selection For the Svm Forecasting Modelmentioning

confidence: 99%

Efficient resource provisioning for elastic Cloud services based on machine learning techniques

et al. 2019

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“…Letp(.) be equal to the SVM Unsupervised selection of optimal parameters for the SVM kernels may be obtained applying the method described in [3]. Since the SVM output is assumed to be an estimate of a pdf, it must be non-negative.…”

Section: Unbiased Pdf Estimation Via Svmmentioning

confidence: 99%

Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition

Trentin

Iorio

2007

Lecture Notes in Computer Science

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Abstract. Classification of structured data (i.e., data that are represented as graphs) is a topic of interest in the machine learning community. This paper presents a different, simple approach to the problem of structured pattern recognition, relying on the description of graphs in terms of algebraic binary relations. Maximum-a-posteriori decision rules over relations require the estimation of class-conditional probability density functions (pdf) defined on graphs. A nonparametric technique for the estimation of the pdfs is introduced, on the basis of a factorization of joint probabilities into individual densities that are modeled, in an unsupervised fashion, via Support Vector Machine (SVM). The SVM training is accomplished applying support vector regression on an unbiased variant of the Parzen Window. The behavior of the estimation algorithm is first demonstrated on a synthetic distribution. Finally, experiments of graphstructured image recognition from the Caltech Benchmark dataset are reported, showing a dramatic improvement over the results (available in the literature) yielded by state-of-the-art connectionist models for graph processing, namely recursive neural nets and graph neural nets.

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“…A key goal of the Raytheon ISP program is to develop the mathematical and information-theoretic infrastructure necessary to implement these approaches in a tactical military environment [1][2][3][4][5][6][7][8]. This has led to one research effort focussed on the development of advanced mathematical approaches for sensor scheduling, sensor allocation and tracking.…”

Section: Introductionmentioning

confidence: 99%

Activities in Integrated Sensing and Processing

Waagen

Schmitt²,

Shah³

Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004.

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Advances in sensor technologies, computation devices and algorithms have created enormous opportunities for intelligent, automated threat identification; target acquisition; and surveillance. Unfortunately, as information requirements grow, conventional network processing techniques require ever-increasing bandwidth between sensors and processors, as well as potentially exponentially complex data reduction methods. Practical approaches will require that the sensing and computation be jointly engineered, to raise the quality of data and classification and to minimize computation, power consumption and cost. This paper summarizes several activities at Raytheon investigating the integration of sensing and processing and presents some preliminary results. ISSNIP 2004295 0-7803-8894-

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<title>Unsupervised optimization of support vector machine parameters</title>

Cited by 7 publications

References 9 publications

Efficient resource provisioning for elastic Cloud services based on machine learning techniques

Efficient resource provisioning for elastic Cloud services based on machine learning techniques

Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition

Activities in Integrated Sensing and Processing

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