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We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selection criterion value always indicates more preferable subset even if the value difference is marginal. This assumption ignores the reliability issues of particular feature preferences, over-fitting and feature acquisition cost. We propose...
We propose a novel linear discriminant analysis method and demonstrate its superiority over existing linear methods. Based on information theory, we introduce a non-parametric estimate of mutual information with variable kernel bandwidth. Furthermore, we derive a gradient-based optimization algorithm for learning the optimal linear reduction vectors which maximizes the mutual information estimate...
New pattern recognition method is considered that is based on ensembles of ”syndromes”. The developed method that is referred to as Multi-model statistically weighted syndromes (MSWS) is further development of earlier Statistically Weighted Syndromes (SWS) method. ”Syndromes” are subregions in space of prognostic features where content of objects from one of the classes differs significantly from...
The standard 2-norm support vector machine (SVM for short) is known for its good performance in classification and regression problems. In this paper, the 1-norm support vector machine is considered and a novel smoothing function method for Support Vector Classification(SVC) and Regression (SVR) are proposed in an attempt to overcome some drawbacks of the former methods which are complex, subtle,...
Support vector machine (SVM) algorithm has shown a good learning ability and generalization ability in classification, regression and forecasting. This paper mainly analyzes the the performance of support vector machine algorithm in the classification problem, including the algorithm in the kernel function selection, parameter optimization, and integration of other algorithms and to deal with multi-classification...
This paper proposes a hybrid feature selection algorithm based on dynamic weighted ant colony algorithm. Features are treated as graph nodes to construct graph model. Ant colony algorithm is used to select features while support vector machine classifier is applied to evaluate the performance of feature subsets, and then feature pheromone is computed and updated based on the evaluation results. At...
Recently, some kinds of extensions of the binary support vector machine (SVM) to multiclass classification have been proposed. In this paper, we focus on the one-against-all and all-together methods, which finally construct the same kind of multiclass classifier. Since in the one-against-all method, binary SVMs are simply combined, the geometric margins of the multiclass classifier are not maximized...
Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for automated classification of human thoughts reflected on an event-related paradigm using fMRI modality with significantly shortened data acquisition time (less...
In the framework of remote-sensing image classification support vector machines (SVMs) have recently been receiving a very strong attention, thanks to their accurate results in many applications and good analytical properties. However, SVM classifiers are intrinsically noncontextual, which represents a severe limitation in image classification. In this paper, a novel method is proposed to integrate...
Computational complexity is one of the most important issues in any machine-learning algorithm. A novel working set selection mechanism is proposed to improve Support Vector Machine (SVM) learning. Implementation is based on the Keerthi et al.'s SMO algorithm, but our approach is one-class classification. When selecting samples for the optimization process, much effort is spent to find the most violating...
This paper concerns lung tissue classification using asymmetric-margin support vector machine (ASVM) to handle the imbalance of the positive and negative classes in a one-against-all multiclass classification problem. The hyperparameters of the algorithm are obtained using an optimization of the upper bound of the leave-one-out error of the ASVM. The ASVM is applied on the dataset with its original...
Classical relevance vector machine is sensitive to outliers during training and has weak robustness. In order to solve this problem, a novel robust relevance vector machine is presented in this paper. The key idea of the proposed method is to introduce individual noise variance coefficient for each training sample. In the process of model training, the noise variance coefficients of outliers gradually...
Scaled Convex Hulls (SCHs) have been recently proposed by Liu et al. as the basis of a method to build linear classifiers that, when extended to kernel settings, provides an alternative approach to more established methods such as SVMs. Here we show how to adapt the Mitchell-Dem'yanov-Malozemov (MDM) algorithm to build such SCH-based classifiers by solving a concrete nearest point problem. We shall...
In nonlinear model predictive control (NMPC), the system performance is greatly dependent upon the accuracy of the predictive model and the efficiency of the online optimization algorithm. In this paper, a novel NMPC scheme with the integration of Support Vector Machine (SVM) and recently proposed general-purpose heuristic “Extremal Optimization (EO)” is presented. With the superior features of self-organized...
The study on Transductive Support Vector Machine (TSVM) has made little progress since Vapnik put forth the concept in the late 1990s, as algorithm for TSVM optimization model can not be easily found. Here we try to transform the problem of TSVM optimization into an unconstrained one before constructing the smooth unconstrained optimization that has a kernel, and on the basis of which to devise a...
A new sparsity-based classification algorithm for hyperspectral imagery is proposed in this paper. This algorithm is based on the assumption that the spectral signatures of pixels in the same class lie in a low-dimensional subspace and thus a test sample can be represented by a sparse linear combination of the training samples. The sparse representation is recovered by solving a constrained optimization...
Traditional method of tender offer is subjective and arbitrary, and the ARIMA Accuracy can't satisfy the tenderer. We have combined the Elman NN with the SVM model to establish a new hybrid optimization algorithm, which are presented to the bidding tender offer in a project. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature...
Efficiency optimization control of salient pole permanent magnet synchronous motor (PMSM) based on maximum ratio of torque to current is presented. From basic equations of PMSM in reference frame, the method of maximum ratio of torque to current was derived using maximum principle. Then, based on the Least Squares Support Vector Machine (LSSVM), modeling of LSSVM controller of maximum ratio of torque...
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