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Twin SVM (TWSVM), as a computationally effective classification tool, is shown to be better than GEPSVM and SVM in favor of classification effectiveness. However, two dual QPPs arising from TWSVM leads to the higher computational time compared to GEPSVM and one has to look for approximate solutions when the data points are very large. In this paper, by slightly reformulating the primal problem of...
The MLSP competition (2010) purpose is to design a pattern recognition system for “mind reading”. This paper is a study of the EEG competition dataset and the crafting of the third place winning method. It shortly presents our signal processing methods for feature extraction, and channel selection. We accurately tuned all the parameters of these preprocessing stage before feeding a Gaussian SVM classifier...
To overcome the shortages of the existing customer classification method such as strict hypothesis, poor generalization ability, low prediction accuracy and low learning rate etc., a method combined of F-scores and support vector machine for customer classification was proposed, and was applied to the problem of bank credit card customer classification. Empirical analysis shows the validation accuracies...
This paper compares the performance of linear and nonlinear kernels of Support Vector Machines (SVM) used for text classification. The study is motivated by the previous viewpoint that linear SVM performs better than nonlinear one, and that, although there are many investigations have proved that SVM performs well in text classification, there is no serious investigation on the comparison between...
A new method for detecting and classifying loudspeaker faults is presented in this paper. Total response of high-order harmonics groups is measured and used as defect features of loudspeaker. Based on support vector machine (SVM), we built a classification system combined with one-class SVM and Directed Acyclic Graphic SVM (DAGSVM). Comparing with K-nearest neighbor (k-NN) classifier, the accuracy...
Protein classification plays an important role in the research in Bioinformatics. Many discriminative methods, including the SVM based algorithms are used to do this job. In order to use these methods, variable length protein sequences must be converted into fixed-length dimensional vectors. The current work presents a new method of converting sequences into vectors. The method first constructs profile...
In this article, we propose a new EEG signal classification method based on Relevance Vector Machine (RVM) and AR model. It can well separate the ictal EEG signals from the inter-ictal ones, this is very important in the diagnosis of epilepsy. Our studies can be divided into three parts: firstly, EEG features were extracted from the signals based on AR models, and then the performance of these features...
Normal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel SVM classification approach for large data sets. It has two phases. In the first phase, an approximate classification is obtained by SVM using fast clustering techniques to select the training data from the original data set. In the...
When the ship is damaged after weapon attack, it is necessary for commanders to recognise its unsinkability grade quickly. Through unsinkability classification, we can know whether the ship will sink or not and its sinking probability. The unsinkability classification is a N-class pattern recognition problem. The fuzzy support vector machine (FSVM) is used to distinguish a certain unsinkability grade...
Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the...
Support vector machines(SVM) is the activest study content in statistical learning theory. Identificating and evaluating the credit risk of bank client Using the technology of SVM has the features of simple arithmetic and high precision. This article firstly introduces the theories and methods of bank client credit risk identification classified basing on SVM, and uses index entropy weighing select...
Supervised Learning (SL) is a machine learning research area which aims at developing techniques able to take advantage from labeled training samples to make decisions over unseen examples. Recently, a lot of tools have been presented in order to perform machine learning in a more straightforward and transparent manner. However, one problem that is increasingly present in most of the SL problems being...
Recent developments in Graphics Processing Units (GPUs) have enabled inexpensive high performance computing for general-purpose applications. Due to GPU's tremendous computing capability, it has emerged as the co-processor of CPU to achieve a high overall throughput. CUDA programming model provides the programmers adequate C language like APIs to better exploit the parallel power of the GPU. K-nearest...
We investigate the effects, in terms of a bias-variance decomposition of error, of applying class-separability weighting plus bootstrapping in the construction of error-correcting output code ensembles of binary classifiers. Evidence is presented to show that bias tends to be reduced at low training strength values whilst variance tends to be reduced across the full range. The relative importance...
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...
We present a new method for the incremental training of multiclass Support Vector Machines that provides computational efficiency for training problems in the case where the training data collection is sequentially enriched and dynamic adaptation of the classifier is required. An auxiliary function that incorporates some desired characteristics in order to provide an upper bound of the objective function...
In this paper, normalized SoP string-edit distances, taking into account all possible alignments between two sequences, are investigated. These normalized distances are variants of the Sum-over-Paths (SoP) distances which compute the expected cost on all sequence alignments by favoring low-cost ones - therefore favoring good alignment. Such distances consider two sequences tied by many optimal or...
This paper introduces a geometrically inspired large-margin classifier that can be a better alternative to the Support Vector Machines (SVMs) for the classification problems with limited number of training samples. In contrast to the SVM classifier, we approximate classes with affine hulls of their class samples rather than convex hulls, which may be unrealistically tight in high-dimensional spaces...
Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of...
The usage of convex hulls for classification is discussed with a practical algorithm, in which a sample is classified according to the distances to convex hulls. Sometimes convex hulls of classes are too close to keep a large margin. In this paper, we discuss a way to keep a margin larger than a specified value. To do this, we introduce a concept of "expanded convex hull" and confirm its...
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