72 research outputs found
Benchmarking least squares support vector machine classifiers.
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.least squares support vector machines; multiclass support vector machines; sparse approximation; discriminant-analysis; sparse approximation; learning algorithms; classification; framework; kernels; time; SISTA;
Farm compost amendment and non-inversion tillage improve soil quality without increasing the risk for N and P leaching
Association of Thyroid Hormone Concentrations with Levels of Organochlorine Compounds in Cord Blood of Neonates
The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts
The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts. June 4-7, 2019, Szczyrk, Polan
Mechanisms of nematode suppression in inundated amended waste soil contaminated with potato cyst nematodes, Globodera rostochiensis and G. pallida
POSTER: Identification, discrimination and classification of Curcuma samples using their chromatographic fingerprints
Awarded with Best poster award.info:eu-repo/semantics/nonPublishe
POSTER: Identification, discrimination and classification of Curcuma samples using their chromatographic fingerprints
info:eu-repo/semantics/nonPublishe
POSTER: Study of the Polygonum influencing PLS discrimination models for Polygonum cuspidatum and Polygonum multiflorum chromatographic fingerprints
info:eu-repo/semantics/nonPublishe
POSTER: Identification, discrimination and classification of herbal samples using their chromatographic fingerprints
info:eu-repo/semantics/nonPublishe
POSTER: Identification, discrimination, and classification of herbal samples using their chromatographic fingerprints
info:eu-repo/semantics/nonPublishe
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