Results 1 to 10 of about 8,989,075 (297)
Support Vector Machine Classifier With Pinball Loss
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quantile distance and the result is less sensitive.
Johan Suykens, Lei Shi, Xiaolin Huang
exaly +4 more sources
Least Squares Support Vector Machine for Minimizing VC Dimensional Expectation Upper Bound [PDF]
Jisuanji kexue yu tansuo, 2023 Machine learning is an important aspect of modern computer technology, and the support vector machine method has been widely used in all walks of life because of its good performance in recent years.
LI Hao, WANG Shitong
doaj +1 more source
Proximal support vector machine classifiers [PDF]
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001 Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assigning them to the closest of two parallel planes (in input or feature space) that are pushed apart as far as possible.
Glenn Fung, Olvi L. Mangasarian
openaire +2 more sources
Least Squares Support Vector Machine Classifiers [PDF]
Neural Processing Letters, 1999 In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s. The approach is illustrated on a two-spiral benchmark classification problem.
Suykens, Johan, Vandewalle, Joos
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Multicategory Proximal Support Vector Machine Classifiers [PDF]
Machine Learning, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Glenn Fung, Olvi L. Mangasarian
openaire +1 more source
Support Vector Machine Procedure as a Data Mining Multi-class Classifier [PDF]
المجلة العراقية للعلوم الاحصائية, 2012 Support vector machine initially developed to perform binary classification. This paper presents a multi-class support vector machine classifier and ordinal regression to classify the type of bone mineral density.
Zakariya Y. Algamal
doaj +1 more source
Jurnal Sisfokom, 2021 Short message service (SMS) adalah salah satu media komunikasi yang penting untuk mendukung kecepatan pengunaan ponsel oleh pengguna. Sistem hibrid klasifikasi SMS digunakan untuk mendeteksi sms yang dianggap sampah dan benar.
Devi Irawan +4 more
doaj +1 more source
Chinese Journal of Mechanical Engineering, 2020 According to statistic data, machinery faults contribute to largest proportion of High-voltage circuit breaker failures, and traditional maintenance methods exist some disadvantages for that issue.
Xiaofeng Li +4 more
doaj +1 more source
Applying machine learning methods to predict geology using soil sample geochemistry
Applied Computing and Geosciences, 2022 In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic
Timothy C.C. Lui +4 more
doaj +1 more source
Benchmarking Least Squares Support Vector Machine Classifiers [PDF]
Machine Learning, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Van Gestel, Tony +7 more
openaire +5 more sources