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1.
多核学习方法   总被引:20,自引:2,他引:18       下载免费PDF全文
多核学习方法是当前核机器学习领域的一个新的热点. 核方法是解决非线性模式分析问题的一种有效方法, 但在一些复杂情形下, 由单个核函数构成的核机器并不能满足诸如数据异构或不规则、样本规模巨大、样本不平坦分布等实际的应用需求, 因此将多个核函数进行组合, 以获得更好的结果是一种必然选择. 本文根据多核的构成, 从合成核、多尺度核、无限核三个角度, 系统综述了多核方法的构造理论, 分析了多核学习典型方法的特点及不足, 总结了各自的应用领域, 并凝炼了其进一步的研究方向.  相似文献
2.
弹性多核学习   总被引:1,自引:0,他引:1       下载免费PDF全文
多核学习 (MKL) 的提出是为了解决多个核矩阵的融合问题, 多核学习求解关于多个核矩阵的最优的线性组合并同时解出对应于这个组合矩阵的支持向量机(SVM)问题. 现有的多核学习的框架倾向于寻找稀疏的组合系数, 但是当有信息的核的比例较高的时候, 对稀疏性的倾向会使得只有少量的核被选中而损失相当的分类信息. 在本文中, 我们提出了弹性多核学习的框架来实现自适应的多核学习. 弹性多核学习的框架利用了一个混合正则化函数来均衡稀疏性和非稀疏性, 多核学习和支持向量机问题都可以视作弹性多核学习的特殊情形. 基于针对多核学习的梯度下降法, 我们导出了针对弹性多核学习的梯度下降法. 仿真数据的结果显示了弹性多核学习方法相对多核学习和支持向量机的优势; 我们还进一步将弹性多核学习应用于基因集合分析问题并取得了有意义的结果; 最后, 我们比较研究了弹性多核学习与另一种利用了非稀疏思想的多核学习.  相似文献
3.
基于多核学习的医学文献蛋白质关系抽取   总被引:1,自引:0,他引:1       下载免费PDF全文
从生物医学文献中抽取蛋白质交互作用关系对蛋白质知识网络的建立、新药的研制等均具有重要的意义。为此,提出一种基于多核学习的方法,用于从文献中自动抽取蛋白质关系信息。该方法融合基于特征的核、树核以及图核,并扩展最短路径依存树以及依存路径以利用更多的上下文关系信息。在AImed语料上的实验得到63.9%的F值和87.83%的AUC值,表明该方法具有较好的性能。  相似文献
4.
多重核学习非线性时间序列故障预报   总被引:1,自引:0,他引:1       下载免费PDF全文
针对非线性时间序列故障预报问题, 提出了多重核学习故障预报方法. 利用多重核学习可以减少支持向量的个数, 提高预测性能. 而且在多重核学习定义的混合核空间中运用减聚类能够提取正常原型. 最后, 将本文提出的方法应用于连续搅拌釜式反应器的故障预报, 仿真结果表明该方法能够提高故障预报的准确性与实时性.  相似文献
5.
根据文本分类通常包含多异类数据源的特点,提出了多核SVM学习算法。该算法将分类核矩阵的二次组合重新表述成半无限规划,并说明其可以通过重复利用SVM来实现有效求解。实验结果表明,提出的算法可以用于数百个核的结合或者是数十万个样本的结合,对于多异类数据源的文本分类具有较高的查全率和查准率。  相似文献
6.
Multiple kernel clustering (MKC), which performs kernel-based data fusion for data clustering, is an emerging topic. It aims at solving clustering problems with multiple cues. Most MKC methods usually extend existing clustering methods with a multiple kernel learning (MKL) setting. In this paper, we propose a novel MKC method that is different from those popular approaches. Centered kernel alignment—an effective kernel evaluation measure—is employed in order to unify the two tasks of clustering and MKL into a single optimization framework. To solve the formulated optimization problem, an efficient two-step iterative algorithm is developed. Experiments on several UCI datasets and face image datasets validate the effectiveness and efficiency of our MKC algorithm.  相似文献
7.
For improving the classification performance on the cheap, it is necessary to exploit both labeled and unlabeled samples by applying semi-supervised learning methods, most of which are built upon the pair-wise similarities between the samples. While the similarities have so far been formulated in a heuristic manner such as by k-NN, we propose methods to construct similarities from the probabilistic viewpoint. The kernel-based formulation of a transition probability is first proposed via comparing kernel least squares to variational least squares in the probabilistic framework. The formulation results in a simple quadratic programming which flexibly introduces the constraint to improve practical robustness and is efficiently computed by SMO. The kernel-based transition probability is by nature favorably sparse even without applying k-NN and induces the similarity measure of the same characteristics. Besides, to cope with multiple types of kernel functions, the multiple transition probabilities obtained correspondingly from the kernels can be probabilistically integrated with prior probabilities represented by linear weights. We propose a computationally efficient method to optimize the weights in a discriminative manner. The optimized weights contribute to a composite similarity measure straightforwardly as well as to integrate the multiple kernels themselves as multiple kernel learning does, which consequently derives various types of multiple kernel based semi-supervised classification methods. In the experiments on semi-supervised classification tasks, the proposed methods demonstrate favorable performances, compared to the other methods, in terms of classification performances and computation time.  相似文献
8.
This paper presents two sets of features, shape representation and kinematic structure, for human activity recognition using a sequence of RGB-D images. The shape features are extracted using the depth information in the frequency domain via spherical harmonics representation. The other features include the motion of the 3D joint positions (i.e. the end points of the distal limb segments) in the human body. Both sets of features are fused using the Multiple Kernel Learning (MKL) technique at the kernel level for human activity recognition. Our experiments on three publicly available datasets demonstrate that the proposed features are robust for human activity recognition and particularly when there are similarities among the actions.  相似文献
9.
Automatic facial landmarking is a crucial prerequisite of many applications dedicated to face analysis. In this paper we describe a two-step method. In a first step, each landmark position in the image is predicted independently. To achieve fast and accurate localizations, we implement detectors based on a two-stage classifier and we use multiple kernel learning algorithms to combine multi-scale features. In a second step, to increase the robustness of the system, we introduce spatial constraints between landmarks. To this end, parameters of a deformable shape model are optimized using the first step outputs through a Gauss–Newton algorithm. Extensive experiments have been carried out on different databases (PIE, LFPW, Cohn-Kanade, Face Pix and BioID), assessing the accuracy and the robustness of the proposed approach. They show that the proposed algorithm is not significantly affected by small rotations, facial expressions or natural occlusions and can be favorably compared with the current state of the art landmarking systems.  相似文献
10.
识别虚假评论有着重要的理论意义与现实价值。先前工作集中于启发式策略和传统的全监督学习算法。最近研究表明:人类无法通过先验知识有效识别虚假评论,手工标注的数据集必定存在一定数量的误例,因此简单使用传统的全监督学习算法识别虚假评论并不合理。容易被错误标注的样例称为间谍样例,如何确定这些样例的类别标签将直接影响分类器的性能。基于少量的真实评论和大量的未标注评论,提出一种创新的PU (positive and unlabeled)学习框架来识别虚假评论。首先,从无标注数据集中识别出少量可信度较高的负例。其次,通过整合LDA(latent Dirichlet allocation)和 K‐means ,分别计算出多个代表性的正例和负例。接着,基于狄利克雷过程混合模型(Dirichlet process mixture model , DPM M ),对所有间谍样例进行聚类,混合种群性和个体性策略来确定间谍样例的类别标签。最后,多核学习算法被用来训练最终的分类器。数值实验证实了所提算法的有效性,超过当前的基准。  相似文献
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