Decomposition techniques for training linear programming support vector machines

The number of fully sequenced fungal genomes is rapidly increasing. Since genetic tools are poorly developed for most filamentous fungi, it is currently difficult to employ genetic engineering for understanding the biology of these fungi and to fully exploit them industrially. For that reason there is a demand for developing versatile methods that can be used to genetically manipulate non-model filamentous fungi. To facilitate this, we have developed a CRISPR-Cas9 based system adapted for use in filamentous fungi. The system is simple and versatile, as RNA guided mutagenesis can be achieved by transforming a target fungus with a single plasmid. The system currently contains four CRISPR-Cas9 vectors, which are equipped with commonly used fungal markers allowing for selection in a broad range of fungi. Moreover, we have developed a script that allows identification of protospacers that target gene homologs in multiple species to facilitate introduction of common mutations in different filamentous fungi. With these tools we have performed RNA-guided mutagenesis in six species of which one has not previously been genetically engineered. Moreover, for a wild-type Aspergillus aculeatus strain, we have used our CRISPR Cas9 system to generate a strain that contains an AACU_pyrG marker and demonstrated that the resulting strain can be used for iterative gene targeting

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;

Using SVM for Classification

Support Vector Machines (SVMs) have found many applications in various fields. They have been introduced for classification problems and extended to regression. In this paper I review the utilization of SVM for classification problems and exemplify this with application on IRIS datasets. I used the Matlab programming language to implement linear and nonlinear classificators and apply this on the dataset

Extensión de la SVM para regresión a tramos

In this paper, a new technique for unidimensional regression based on support vector machines is presented. The method is based on the addition of new linear restrictions to the standard (or global) support vector machines technique. Despite the new linear restrictions, a quadratic programming problem is also obtained. Moreover, the use of support vector machines allows us a straightforward derivation of a nonlinear extension of the proposed technique. Finally, simulation results are presented in which the proposed technique is applied to an equalization problem, in this example the proposed technique presents similar results to those of the Bayesian (optimal) equalizer. The technique is also applied to a nonlinear regression problem in which local modeling provides better results than global modeling

A SPARSE LEAST SQUARES SUPPORT VECTOR MACHINE CLASSIFIER

In the last decade Support Vector Machines (SVM) - introduced by Vapnik - have been successfully applied to a large number of problems. Lately a new technique, the Least Squares SVM (LS-SVM) has been introduced, which addresses classification and regression problems by formulating a linear equation set. In comparison to the original SVM, which involves a quadratic programming task, LS-SVM simplifies the required computation, but unfortunately the sparseness of standard SVM is lost. The linear equation set of LS-SVM embodies all available information about the learning process. By applying modifications to this equation set, we present a Least Squares version of the Least Squares Support Vector Machine (LS2-SVM). The modifications simplify the formulations, speed up the calculations and provide better results, but most importantly it concludes a sparse solution

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers.Supervised classification, Binarization, Column generation, Support vector machines

Non linear programming for stochastic dynamic programming

International audienceMany stochastic dynamic programming tasks in continuous action-spaces are tackled through discretization. We here avoid discretization; then, approximate dynamic programming (ADP) involves (i) many learning tasks, performed here by Support Vector Machines, for Bellman-function-regression (ii) many non-linearoptimization tasks for action-selection, for which we compare many algorithms. We include discretizations of the domain as particular non-linear-programming-tools in our experiments, so that by the way we compare optimization approaches and discretization methods. We conclude that robustness is strongly required in the non-linear-optimizations in ADP, and experimental results show that (i) discretization is sometimes inefficient, but some specific discretization is very efficient for "bang-bang" problems (ii) simple evolutionary tools outperform quasi-random in a stable manner (iii) gradient-based techniques are much less stable (iv) for most high-dimensional "less unsmooth" problems Covariance-Matrix-Adaptation is first ranked