30143163
A Fast and Accurate Progressive Algorithm for Training Transductive SVMs
This paper develops a fast and accurate algorithm for training transductive SVMs classifiers, which utilizes the classification information of unlabeled data in a progressive way. For improving the generalization accuracy further, we employ three important criteria to enhance the algorithm, i.e. confidence evaluation, suppression of labeled data, stopping with stabilization. Experimental results on several real world datasets confirm the effectiveness of these criteria and show that the new algorithm can reach to comparable accuracy as several state-of-the-art approaches for training transductive SVMs in much less training time.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
36695959
Selective Ensemble Algorithms of Support Vector Machines Based on Constraint Projection
This paper proposes two novel ensemble algorithms for training support vector machines based on constraint projection technique and selective ensemble strategy. Firstly, projective matrices are determined upon randomly selected must-link and cannot-link constraint sets, with which original training samples are transformed into different representation spaces to train a group of base classifiers. Then, two selective ensemble techniques are used to learn the best weighting vector for combining them, namely genetic optimization and minimizing deviation errors respectively. Experiments on UCI datasets show that both proposed algorithms improve the generalization performance of support vector machines significantly, which are much better than classical ensemble algorithms, such as Bagging, Boosting, feature Bagging and LoBag.
38516500
A New Approach of Network Intrusion Detection Using HVDM-Based SOM
The research of applying Self-organizing Maps for intrusion detection is investigated in this paper. A novel approach is presented for enhancing SOM's abilities of identifying temporal network attacks, which combine with FIR filter. Meanwhile, we reconsider the heterogeneous dataset that composed of network connection's features, and select HVDM as the distance function determining the winning neuron during SOM's training and testing. In the end, KDD benchmark dataset is employed to validate the efficiency of our approach, and the results is detection rates of 96.5%, false positive rates of 6.2%, which accounts for good performance of our new approach in intrusion detection fields.
14748707
Training One-class Support Vector Machines in the Primal Space
In this paper, we proposed a novel Newton-type solver for one-class support vector machines in the primal space directly. Firstly, utilizing reproducing property of kernel and Huber regression function, original constrained quadratic programming is transformed into approximate unconstrained one, which is continuous and twice differentiable. Then, we give a Newton-type training algorithm to solve it. Further analysis shows the computation complexity of our algorithm is identical with theoretical lower bound for solving one-class support vector machines. In the end, experiments on 9 UCI datasets are done to validate the affectivity of proposed algorithm, and when comparing with dual method (LIBSVM),its produces comparative testing accuracy, better training speed, and less support vectors.
9035268
Optimally Balanced Forward Degree Sequence
Forward degree seqences, arising from orderings of the vertices in a graph, carry a lot of vital information about the graph. In this paper, we focus our work on two special classes of forward degree sequences, which we named balanced and strongly balanced. Our main result is to prove that any chordal graph has a strongly balanced forward degree sequence and any graph with all degrees at most 3 has a balanced forward degree sequence. Moreover, we show that the strongly balanced forward degree sequence can be computed in polynomial time in the above cases.
5903999
Texture classification using multiresolution Markov random field models
Abstract Texture classification is an important topic in texture analysis. Texture classification has wide applications in remote sensing, computer vision, and image analysis. During the past years, several authors discussed to use multiresolution stochastic approaches to model textures. However, in these approaches, the highpass components which contain rich detailed information are lost. In this paper, we propose multiresolution MRF (MRMRF) modeling to describe textures. MRMRF modeling is a method trying to fuse filtering theory and MRF models. In the MRMRF modeling, highpass components are considered as well as lowpass components. “Brodatz texture database” is used in this paper for the experiments and Nearest Linear Combination (NLC) is used as measurement of distance to improve the recognition rate. The experimental results show that NLC has much better performance than Nearest Neighbor (NN) as the measurement in MRMRF modeling.
30078088
Image Segmentation by a Robust Clustering Algorithm Using Gaussian Estimator
This paper presents a novel clustering-based image segmentation method, which incorporates the features of robust statistics. To overcome the sensitivity to noise and outliers in fuzzy clustering, a simple but efficient M-estimator, Gaussian estimator, has been introduced to clustering analysis as weight or membership function. When applied to image analysis, the proposed Robust Gaussian Clustering/Segmentation (RGCS) algorithm exhibits more reasonable pixel classification and noise suppression performance with respect to FCM. Moreover, by using a uniform resolution parameter scheme, this method avoids producing coincident clusters, which occurs often in possibilistic clustering segmentation. Experiments on both the synthetic data and real image demonstrate the validity and power of the proposed algorithm.
18513431
Modeling and Properties of Nonlinear Stochastic Dynamical System of Continuous Culture
The stochastic counterpart to the deterministic description of continuous fermentation with ordinary differential equation is investigated in the process of glycerol bio-dissimilation to 1,3-propanediol by Klebsiella pneumoniae. We briefly discuss the continuous fermentation process driven by three-dimensional Brownian motion and Lipschitz coefficients, which is suitable for the factual fermentation. Subsequently, we study the existence and uniqueness of solutions for the stochastic system as well as the boundedness of the Two-order Moment and the Markov property of the solution. Finally stochastic simulation is carried out under the Stochastic Euler-Maruyama method.
12587822
Clustering Research Based on Density Gradient
Cluster analysis, intending to help users understand the natural grouping or structure in a data set, has received a lot of attention in the last few years. Aimed to solve difficult problems in clustering with irregularly distributed data set, a new clustering algorithm based on density gradient is provided. With analysis of density of each point and its neighbors, the algorithm searches points with the maximum density and takes them as centers of original clusters. Then it combines some little clusters into larger clusters according to the distribution of boundary points between neighbor clusters. Experimental results show that the new algorithm has better performance than DBSCAN.