Support vector classification with parameter tuning assisted by agent-based technique

Kulkarni, Abhijit J.; Jayaraman, Vaidyanathan; Kulkarni, Bhaskar D.

doi:10.1016/s0098-1354(03)00188-1

Cited by 55 publications

(23 citation statements)

References 19 publications

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“…Support Vector Machine (SVM) introduced first by Cortes [24] as a universal feed-forward network-based classification algorithm based on the statistical learning theory and structural risk minimization principle [25]. SVM approach is a supervised learning method with associated learning algorithm that analyzes data and recognizes patterns of input/output data.…”

Section: Modeling Tool -Support Vector Machinementioning

confidence: 99%

Experimental investigation and development of a SVM model for hydrogenation reaction of carbon monoxide in presence of Co–Mo/Al2O3 catalyst

Anbari

Adib

Iranshahi

2015

Chemical Engineering Journal

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Section: Modeling Tool -Support Vector Machinementioning

confidence: 99%

Experimental investigation and development of a SVM model for hydrogenation reaction of carbon monoxide in presence of Co–Mo/Al2O3 catalyst

Anbari

Adib

Iranshahi

2015

Chemical Engineering Journal

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“…SVM is being extensively used for several classification and regression applications. As the theory is well developed, we provide only the basic ideas [12,13] involved in binary classification.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Arrhythmia Classification Using Local Hölder Exponents and Support Vector Machine

Joshi

Rajshekhar

Chandran

et al. 2005

Lecture Notes in Computer Science

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Abstract. We propose a novel hybrid Hölder-SVM detection algorithm for arrhythmia classification. The Hölder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.

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“…On the other hand, at study [13] parameter values are optimized for only one data set of them. At [15] an agent based Genetic-Quasi-Newton algorithm was used to minimize radius and span bound for leave-one-out errors on Rayon and Wine data sets. Derivative-free numerical optimizer with a new quality measure for parameters tuning on breast cancer data set was introduced at [16].…”

Section: Introductionmentioning

confidence: 99%

A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases

Çomak

Arslan

2010

J Med Syst

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Classification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and σ). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure.

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Support vector classification with parameter tuning assisted by agent-based technique

Cited by 55 publications

References 19 publications

Experimental investigation and development of a SVM model for hydrogenation reaction of carbon monoxide in presence of Co–Mo/Al2O3 catalyst

Experimental investigation and development of a SVM model for hydrogenation reaction of carbon monoxide in presence of Co–Mo/Al2O3 catalyst

Arrhythmia Classification Using Local Hölder Exponents and Support Vector Machine

A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases

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