[PDF] Top 20 Robustness and Regularization of Support Vector Machines
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Robustness and Regularization of Support Vector Machines
... In this paper we consider a different setup, assuming that the training data are generated by the true underlying distribution, but some non-i.i.d. (potentially adversarial) disturbance is then added to the samples we ... See full document
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Improved Natural Language Learning via Variance Regularization Support Vector Machines
... so regularization plays less of a ...the regularization penalty from the bias weight, letting the optimization freely set the weight to minimize training ... See full document
10
Bouligand Derivatives and Robustness of Support Vector Machines for Regression
... In this paper, we will prove that many SVMs based on Lipschitz continuous loss functions have a bounded Bouligand influence function. To formulate our results we will use Bouligand-derivatives in the sense of Robinson ... See full document
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Laplacian Support Vector Machines Trained in the Primal
... The proposed stopping criteria could be exploited in the optimization of alternative formulations of the LapSVM problem (following the improved models of Abernethy et al., 2008 and of Tsang and Kwok, 2006), with the aim ... See full document
36
Infinite ensemble learning with support vector machines
... Table 5.3 adds hard-margin SVM into comparison for the artificial datasets. That is, we present the result for C = ∞ in SVM-Stump in the second column. First, we can see that the hard-margin SVM-Stump performs worse than ... See full document
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Quadratic Surface Support Vector Machines with Applications.
... To be more robust to initialization, soft clustering methods are proposed, just like finite mixture fitting using the expectation-maximization (EM) algorithm [38], which is more effective than K-means algorithm in ... See full document
113
Sparse Deconvolution Using Support Vector Machines
... of robustness against the presence of non-Gaussian additive ...the support vector formulation, and the implicit regularization of all SVM algorithms helps us to obtain interesting ... See full document
13
Clustering Via Supervised Support Vector Machines
... The choices of the SVM regularization constant, C, has a profound distinction when used in the context of SVM-Relabeler. The choice of C in the supervised SVM controls the trade off between the training error and ... See full document
93
Support vector machines with adaptive Lq penalty
... in (2) as a function of (λ, q). For a fixed q, the prior distribution with a larger λ tends to put more mass around 0. This amounts to putting a larger weight on the regularization term. For a fixed λ of ... See full document
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Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity
... Sant’Anna et al. (2015) proposed a solution for non-linear problems; they used Artificial Neural Networks (ANNs)(Silva et al., 2010) for the genetic classification of simulated hybrid populations. They observed up to ... See full document
10
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
... of regularization that allows us to exploit the geometry of the marginal ...including support vector machines and regularized least squares can be obtained as special ... See full document
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Online Full Text
... the support vector machine are ...The support vector machine [10] [11] is one of AI techniques which serve as classifier in the ...a support vector machine is to construct a ... See full document
5
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 ... See full document
7
Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to pro[r] ... See full document
15
Prediction of soil physical properties by optimized support vector machines
... optimized support vector machines with simulated annealing algorithm in developing pre- diction functions for estimating soil aggregate stability and soil shear strength was ...of support ... See full document
7
Support Vector Machines for Design Space Exploration
... Abstract—To find a suitable model of a system it is usually necessary to conduct time consuming and expensive measure- ments. Design of Experiments (DoE) methods are then often used with the goal to find the best ... See full document
6
Online Full Text
... Fuzzy Support Vector Machines (PSOFuzzySVM) to predict oil well gas lift performance and production optimization in a ...Fuzzy Support Vector Machines (FuzzySVM), which is a ... See full document
7
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 ... See full document
45
A Hierarchy of Support Vector Machines for Pattern Detection
... We presented a general method for exploring a space of hypotheses based on a coarse-to-fine hier- archy of SVM classifiers and applied it to the special case of detecting faces in cluttered images. As opposed to a single ... See full document
37
Areas categorization by operating Support Vector Machines
... For utilizing SVM to multi-class categorizations, two main approaches have been proposed. The fundamental idea is to decrease the multi-class to a set of binary problems so that the SVM approach can be used. The first ... See full document
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