Support Vector Machines (SVMs)
Robustness and Regularization of Support Vector Machines
... Support Vector Machines (SVMs for short) originated in Boser et al. (1992) and can be traced back to as early as Vapnik and Lerner (1963) and Vapnik and Chervonenkis (1974). They continue to be one ...
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Choosing Multiple Parameters for Support Vector Machines
... Abstract. The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization ...
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Support vector machines in projects risk classification
... using Support Vector Machines with advantages over traditional methods, since effects such as reverse classification and ambiguity in the risk hierarchy are ...
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Areas categorization by operating Support Vector Machines
... In recent years, Support Vector Machines (SVMs) have demonstrated excellent functioning in a variety of area categorization problems. This paper explains areas categorization by operating SVMs. The ...
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Support vector machines for texture classification
... Abstract—This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level ...
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Bankruptcy prediction with support vector machines
... of support vector machines (SVMs) for prediction of German companies’ failure that is based on 24 financial ratios being grouped into four categories, namely profitability, leverage, liquidity and ...
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Rating Companies with Support Vector Machines
... the support vector machine (SVM) – to the field of corporate bankruptcy ...that support vector machines are capable of extracting useful information from financial data although ...
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Consensus-Based Distributed Support Vector Machines
... train support vector machines when training data are distributed across different nodes, and their communication to a centralized processing unit is prohibited due to, for example, communication ...
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Support Vector Machines for Design Space Exploration
... For linear problems factor analysis or principal component analysis (PCA) are well established methods for handling high dimensional data and performing dimensionality reduc- tion, e.g. as preprocessing tools for solving ...
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Extracting Important Sentences with Support Vector Machines
... Extracting sentences that contain important in- formation from a document is a form of text summarization. The technique is the key to the automatic generation of summaries similar to those written by humans. To achieve ...
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A Tutorial on Support Vector Machines for Pattern Recognition
... linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in ...how support vector training can be practically implemented, and ...
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Scalable Multilevel Support Vector Machines
... Training nonlinear support vector machines (SVM) is often a time consuming task when the data is big. This problem becomes extremely sensitive when the model selection techniques are applied as both ...
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Support Vector Machines and Multi-Task Learning
... side, Support Vector Machines (SVMs) are popular models in machine learning due to its multiple characteristics and the theory that supports ...
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Support Vector Machines in R
... Support Vector learning is based on simple ideas which originated in statistical learning theory (Vapnik ...that Support Vector Machines (SVMs) apply a simple linear method to the data ...
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Estimating probabilities of default with support vector machines
... The correct estimation of the firms’ insolvency risk has gained an ever in- creasing importance in corporate finance, especially in the age of Basel II. Parallel with the importance of insolvency prognosis the demands for ...
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Chunking with Support Vector Machines
... We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization perfor- mance even with input data of high dimensional feature spaces. ...
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On qualitative robustness of support vector machines
... Support vector machines (SVMs) have attracted much attention in theoretical and in applied statistics. The main topics of recent interest are consistency, learning rates and robustness. We address ...
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Sparseness of Support Vector Machines
... Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called ...
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Support Vector Machines with Convex Combination of Kernels
... Support Vector Machines are supervised learning methods that analyze data for classification and regression. SVM was first suggested in the 1990s by Bernhard E. Boser, Isabelle M. Guyon, and Vladimir ...
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Numerical Experiments with Support Vector Machines
... the Support Vector Machines for the environmental and pollution spatial data classification has been considered in our previous papers (Kanevski et al 1999, Gilardi and Kanevski ...
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