pLink is a search engine for high-throughput identification of cross-linked peptides from their tandem mass spectra, which is the data-analysis step in chemical cross-linking of proteins coupled with mass spectrometry...
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In multi-label classification, labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the ...
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In multi-label classification, labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the global label correlations. However, the label correlations may be different over different data groups. In this paper, we propose a simple and efficient framework for multi-label classification, called Group sensitive Classifier Chains. We assume that similar examples not only share the same label correlations, but also tend to have similar labels. We augment the original feature space with label space and cluster them into groups, then learn the label dependency graph in each group respectively and build the classifier chains on each group specific label dependency graph. The group specific classifier chains which are built on the nearest group of the test example are used for prediction. Comparison results with the state-of-the-art approaches manifest competitive performances of our method.
As the third-generation neural network technology, pulse coupled neural network (PCNN) had used in many fields successfully, but it hindered its popularize that so many parameters of the PCNN need to be set up. This p...
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The growing study in RGB-D sensor and 3D point cloud have made new progress in obstacle avoidance for the visually impaired. However, it remains a challenging problem due to the difficulty in design a robust and real-...
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The AGM postulates are for the belief revision (revision by a single belief), and the DP postulates are for the iterated revision (revision by a finite sequence of beliefs). Li (The Computer Journal 50:378–390, 2007)...
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Although significant success has been achieved in fine-grained visual categorization, most of existing methods require bounding boxes or part annotations for training and test, resulting in limited usability and flexi...
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Although significant success has been achieved in fine-grained visual categorization, most of existing methods require bounding boxes or part annotations for training and test, resulting in limited usability and flexibility. To conquer these limitations, we aim to automatically detect the bounding box and parts for fine-grained object classification. The bounding boxes are acquired by a transferring strategy which infers the locations of objects from a set of annotated training images. Based on the generated bounding box, we propose a multiple-layer Orientational Spatial Part (OSP) model to generate a refined description for the object. Finally, we employ the output of deep Convolutional Neural Network (dCNN) as the feature and train a linear SVM as object classifier. Extensive experiments on public benchmark datasets manifest the impressive performance of our method, i.e., Classification accuracy achieves 63.9% on CUB-200-2011 and 75.6% on Aircraft, which are actually higher than many existing methods using manual annotations.
ABGP is a special cognitive model, which consists of awareness, beliefs, goals and plans. As most agent architectures, ABGP agents obtain knowledge from the natural scenes only through single preestablished rules as w...
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ISBN:
(纸本)9781509001644
ABGP is a special cognitive model, which consists of awareness, beliefs, goals and plans. As most agent architectures, ABGP agents obtain knowledge from the natural scenes only through single preestablished rules as well, don't directly capture the natural scenes information like human visual. Inspired by the biological visual cortex (V1) and the higher brain areas perceiving visual features, we propose a novel deep network model convolutional generative stochastic model (CGSM) used to visual feature representation, and firstly introduce it into the awareness module of the cognitive model ABGP to construct a state-of-the-art cognitive model ABGP-CGSM. For the novel cognitive model ABGP-CGSM, we construct a rat-robot maze search simulation platform to show the validity recognizing natural scenes. According to the simulation results on the noise and noiseless natural scenes, the rat-robot implemented by ABGP-CGSM has an excellent success rate when passing through the maze. The simulation shows that the ABGP-CGSM model proposed in our work can directly enhance the capability of communication between agent and natural scenes, improve the ability to cognize the real world as human being and conduct the agent to plan independently its path in terms of the visual information from the natural scenes.
＜正＞Cryo-electron tomography（ET）plays an important role in revealing biological structures,ranging from macromolecule scale to subcellular *** acquiring series of pictures with different angular assignment,one per...
＜正＞Cryo-electron tomography（ET）plays an important role in revealing biological structures,ranging from macromolecule scale to subcellular *** acquiring series of pictures with different angular assignment,one performs an inverse radon transform and gets the 3D structure of biological ***,since high angle
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