A method for multi-classifier ensemble of Support Vector Machine ensemble (SVMs) and Kernel Matching Pursuit Ensemble (KMPs) is proposed. Support Vector Machine has advantage in solving classification problem of high ...
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A method for multi-classifier ensemble of Support Vector Machine ensemble (SVMs) and Kernel Matching Pursuit Ensemble (KMPs) is proposed. Support Vector Machine has advantage in solving classification problem of high dimension and small size dataset, and Kernel Matching Pursuit has almost classified performance and the more sparsely solution as comprised with the SVM. So the SVM and the KMP are mix boosted in this paper, which can decrease generalization errors of the single classifier ensemble and improve ensemble classification accuracy by increasing diversity between ensemble individuals. The experiments show that the proposed method can shorten running time and improve classification accuracy compared with individual SVMs or KMPs.
Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems...
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Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition. Only a part of samples or support vectors (SVs) plays an important role in the final decision function. But SVs could not be obtained in advance until a quadratic programming is performed. In this paper, we use K-nearest neighbour method to extract a boundary vector set which may contain SVs. The number of the boundary set is smaller than the whole training set. Consequently it reduces the training samples, speeds up the training of support vector machine.
Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new appr...
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Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new approach brings in prior information at the proper time, updates scan path dynamically, needs less computational resources and reduces the probability to direct the attention to a less-meaning area. The application to search an airport target in remote sensing image was provided, through which the novel mechanism that how visual attention chose the area was described. Compared with another two region extraction models, experimental results confirm the effectiveness of the approach proposed in this paper.
A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad ...
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A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).
Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal appro...
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Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.
In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the infor...
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In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the clonal selection algorithm (CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.
This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighbor...
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This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighborhood is represented as a support vector hypersphere, and an adaptive learning coefficient is presented. The input data set is firstly divided into subsets by antibodies, then each subset is mapped into a hypersphere respectively in a high dimensional feature space by support vector domain description. Finally the clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree, which need not a predefined number of clustering. The experimental results with several data sets illustrate the effectiveness of the proposed algorithm.
To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave an...
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To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave and long-wave infrared images and detect targets. Firstly, the source images are decomposed by wavelet transformation. In usual, targets in infrared images are man-made, and their fractal dimension is different comparing with natural background. In wavelet transformation domain high-frequency part, we calculate local fractal dimension and set up fusion rule to merge corresponding sub-images of two matching source images. In low-frequency, we extract local maximum gray level to fuse them. Then reconstruct image by wavelet inverse transformation and obtain fused result image. In fusion results, the contrast between targets and background has obvious changes. And targets can be detected using contrast threshold. The experimental results show that the method proposed in this paper using wavelet transformation fractal dimension to fuse dual band infrared images, and then detect targets is better than using mid-wave or long -wave infrared images detect targets alone.
Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clusterin...
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Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clustering ensemble algorithm (MECEA) to perform the texture image segmentation. MECEA comprises two main phases. In the first phase, MECEA uses a multiobjective evolutionary clustering algorithm to optimize two complementary clustering objectives: one based on compactness in the same cluster, and the other based on connectedness of different clusters. The output of the first phase is a set of Pareto solutions, which correspond to different tradeoffs between two clustering objectives, and different numbers of clusters. In the second phase, we make use of the meta-clustering algorithm (MCLA) to combine all the Pareto solutions to get the final segmentation. The segmentation results are evaluated by comparing with three known algorithms: K-means, fuzzy K-means (FCM), and evolutionary clustering algorithm (ECA). It is shown that MECEA is an adaptive clustering algorithm, which outperforms the three algorithms in the experiments we carried out.
DNA tile self-assembly has been proved to enable programmable manipulation of biological systems as a tool of molecular computation. It is mainly based on the property that is the spontaneous self-ordering of substruc...
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DNA tile self-assembly has been proved to enable programmable manipulation of biological systems as a tool of molecular computation. It is mainly based on the property that is the spontaneous self-ordering of substructure into superstructure driven by annealing of Watson-Crick base-pairing DNA sequences. We take full advantage of the superiority of DNA tile self-assembly to construct a molecular computing system that implements a solution for the 0-1 planning problem. This algorithm can independently and simultaneously yield the data pool, containing all possible solutions when all basic operation tiles are designed beforehand. Then we can use some advanced bio-chemistry techniques to select the optimization solutions of the 0-1 planning problem. Our work has shown that it is possible to work with an exponential number of components to solve NP-complete problems. The method proposed here also can reduce the number of laboratory steps required for computation so that it can improve the computation speed.
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