Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

Anguita, Davide; Ghio, Alessandro; Greco, Noemi; Oneto, Luca; Ridella, Sandro

doi:10.1109/ijcnn.2010.5596450

Cited by 94 publications

(50 citation statements)

References 22 publications

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“…AHP is used to determine weight criteria and subcriteria as it assists in process computation and to determine the main criteria [15]. Classification using SVM because SVM has advantages like the curse of dimensionality to solve the problem for have few data like in this case [16]. TOPSIS is used to determine recommendations as it is the most simple method in maximizing the distance from negative ideals and in minimizing the distance for positive ideals [17].…”

Section: Ahp-svm-topsismentioning

confidence: 99%

Selection and Recommendation Scholarships Using AHP-SVM-TOPSIS

Putra

Ariyanti

Cholissodin

2016

JITeCS

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Abstract. Gerakan Nasional Orang Tua Asuh Scholarship offers a number of scholarship packages. As there are a number of applicants, a system for selection and recommendation is required. we used 3 methods to solve the problem, the methods are AHP for feature selection, SVM for classification from 3 classes to 2 classes, and then TOPSIS give a rank recommendation who is entitled to receive a scholarship from 2 classes. In testing threshold for AHP method the best accuracy 0.01, AHP selected 33 from 50 subcriteria. SVM has highest accuracy in this research is 89.94% with Sequential Training parameter are λ =0.5, constant of γ =0.01 , ε = 0.0001, and C = 1. Keywords: Selection, Recommendation, Scholarships, AHP-SVM-TOPSIS

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Section: Ahp-svm-topsismentioning

confidence: 99%

Selection and Recommendation Scholarships Using AHP-SVM-TOPSIS

Putra

Ariyanti

Cholissodin

2016

JITeCS

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“…C low = C 14: end if 15: end while 16: return w * (C) A third approach is based on the ideas of Martein and Schaible (1987) and Anguita et al (2010). It exploits, in turn, conventional Linear (LP) and Quadratic Programming (QP) optimization algorithms, as shown in Algorithm 4.…”

Section: Training a Classifier With I-svmmentioning

confidence: 99%

“…Algorithm 4 Algorithm for solving I-SVM Problem (73) based on the results of Martein and Schaible (1987) and Anguita et al (2010) Require: We iteratively proceed in solving the dual of Problem (98) and updating the value of γ 0 until the termination condition is met:…”

Section: Training a Classifier With I-svmmentioning

confidence: 99%

Tikhonov, Ivanov and Morozov regularization for support vector machine learning

2015

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Learning according to the structural risk minimization principle can be naturally expressed as an Ivanov regularization problem. Vapnik himself pointed out this connection, when deriving an actual learning algorithm from this principle, like the well-known support vector machine, but quickly suggested to resort to a Tikhonov regularization schema, instead. This was, at that time, the best choice because the corresponding optimization problem is easier to solve and in any case, under certain hypothesis, the solutions obtained by the two approaches coincide. On the other hand, recent advances in learning theory clearly show that the Ivanov regularization scheme allows a more effective control of the learning hypothesis space and, therefore, of the generalization ability of the selected hypothesis. We prove in this paper the equivalence between the Ivanov and Tikhonov approaches and, for the sake of completeness, their connection to Morozov regularization, which has been show to be useful when effective estimation of the noise in the data is available. We also show that this equivalence is valid under milder conditions on the loss function with respect to Vapnik's original proposal. These results allows us to derive several methods for performing SRM learning according to an Ivanov or Morozov regularization scheme, but using Tikhonovbased solvers, which have been thoroughly studied in the last decades and for which very efficient implementations have been proposed.

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“…Data Analysis is improving our ability to understand complex phenomena much more rapidly than a priori physical models have done in the past (Anguita et al, 2010;Peng et al, 2010). Real-world systems are usually very complex and influenced by many exogenous factors, which make them very challenging to model, relying solely on the a priori knowledge of the problem (Witten et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A novelty detection approach to diagnosing hull and propeller fouling

et al. 2019

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Hull and propeller performance have a primary role in overall vessel efficiency. Vessel fouling is a common phenomenon where undesirable substances attach or grow on the ship hull. A clear understanding of the extent of the degradation of the hull will allow better management of assets and prediction of the best time for dry docking and hull maintenance work. In this paper, the authors investigate the problems of predicting the hull condition in real operations based on data measured by the on-board systems. The proposed solution uses an unsupervised Machine Learning (ML) modelling technique to eliminate the need for collecting labeled data related to the hull and propeller fouling condition. Two anomaly detection methods based on Support Vector Machines and k-nearest neighbour have been applied to predict the hull condition using the available parameters measured on-board. Data from the Research Vessel The Princess Royal has been exploited to show the effectiveness of the proposed methods and to benchmark them in a realistic maritime application.

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Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

Cited by 94 publications

References 22 publications

Selection and Recommendation Scholarships Using AHP-SVM-TOPSIS

Selection and Recommendation Scholarships Using AHP-SVM-TOPSIS

Tikhonov, Ivanov and Morozov regularization for support vector machine learning

A novelty detection approach to diagnosing hull and propeller fouling

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