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检索条件"主题词=Pursuit Algorithms"

共 488 条记录，以下是181-190 订阅

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Scoring Models for Insurance Risk Sharing Pool Opimization

Scoring Models for Insurance Risk Sharing Pool Opimization

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Nicolas Chapados Charles Dugas Pascal Vincent Réjean Ducharme Dép. d'informatique et recherche opérationnelle and ApSTAT Technologies Université de Montreal Canada Dép. de mathématiques et de statistique and ApSTAT Technologies Université de Montreal Canada ApSTAT Technologies Inc. Montreal Canada

We introduce a flexible scoring model that can be used by property and casualty insurers that have access to a risk-sharing pool to better select the insureds to transfer to the pool. The model discriminates between insureds whose transfer is likely to be profitable under the pool regulations against those paying a fair premium. This model makes use of feature selection methods to automatically discover the most relevant model inputs, yet is robust to overfitting due to the use of a rank averaging technique. By analogy to the knapsack problem, we show what should be the most suitable sorting criterion depending on the pool regulations. We illustrate the performance of the approach by testing against the historical data of a mid-sized Canadian insurer.

关键词： Insurance Automobiles Data mining Books Profitability pursuit algorithms Conferences Robustness Sorting Testing

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A New Gradient Based Algorithm for Kernel Machine Classifier

A New Gradient Based Algorithm for Kernel Machine Classifier

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Southeastern Symposium on System Theory

作者： Ali Reza Bayesteh Tashk Amir Babaeean Kourosh Dadashtabar Farid Samsami Khodadad Amirkabir University of Technology Tehran Tehran IR Department of Electrical Engineering Amirkabir University of Technology슠 Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering Malekashtar University of Technology Tehran Iran

ISBN: (纸本)9781424418060

A new greedy algorithm is introduced using basic matching pursuit based on minimization of the mean square error (MSE) criterion. Comparing with its previous counterparts i.e. support vector machine (SVM) and relevance vector machine (RVM), in our proposed approach, the kernel mean is not restricted to the training input data. However, in this paper, the kernel mean is chosen in an adaptive manner based on the so-called gradient descent algorithm. The experimental results reveal that the proposed gradient kernel construction outperforms other previous algorithms in terms of scarcity and generalization.

关键词： Kernel Matching pursuit algorithms Support vector machines Support vector machine classification Dictionaries pursuit algorithms Mean square error methods Greedy algorithms Minimization methods Robustness

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MDL, penalized likelihood, and statistical risk

MDL, penalized likelihood, and statistical risk

引用

IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Andrew R. Barron Cong Huang Jonathan Q. Li Xi Luo Statistics Department Yale University New Heaven CT USA Radar Networks Incorporate San Francisco CA USA

We determine, for both countable and uncountable collections of functions, information-theoretic conditions on a penalty pen(f) such that the optimizer f of the penalized log likelihood criterion log 1/likelihood(f)+pen(f) has risk not more than the index of resolvability corresponding to the accuracy of the optimizer of the expected value of the criterion. If F is the linear span of a dictionary of functions, traditional description-length penalties are based on the number of non-zero terms (the lscr 0 norm of the coefficients). We specialize our general conclusions to show the lscr 1 norm of the coefficients times a suitable multiplier lambda is also an information-theoretically valid penalty.

关键词： Dictionaries Statistics Information theory Data compression Risk analysis pursuit algorithms Approximation error Radar Statistical distributions Probability distribution

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Sparse Non-Negative Solution of a Linear System of Equations is Unique

Sparse Non-Negative Solution of a Linear System of Equations...

引用

3rd International Symposium on Communications, Control, and Signal Processing (ISCCSP 2008), vol.2

作者： Alfred M. Bruckstein Michael Elad Michael Zibulevsky Computer Science Department Technion-Israel Institute of Technology Haifa Israel

We consider an underdetermined linear system of equations Ax = b with non-negative entries of A and b, and the solution x being also required to be non-negative. We show that if there exists a sufficiently sparse solution to this problem, it is necessarily unique. Furthermore, we present a greedy algorithm - a variant of the matching pursuit - that is guaranteed to find this sparse solution. The result mentioned above is obtained by extending the existing theoretical analysis of the Basis pursuit problem, i.e. min ‖x‖{sub}1 s.t. Ax = b, by studying conditions for perfect recovery of sparse enough solutions. Considering a matrix A with arbitrary column norms, and an arbitrary monotone element-wise concave penalty replacing the l{sub}1-norm objective function, we generalize known equivalence results, and use those to derive the above uniqueness claim.

关键词： Linear systems Equations Sparse matrices Computer science Lead pursuit algorithms Greedy algorithms Matching pursuit algorithms Signal processing Image processing

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Deformable template combining alignable and non-alignable sketches

Deformable template combining alignable and non-alignable sk...

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International Conference on Pattern Recognition

作者： Linjie Zhang Haifeng Gong Tianfu Wu Junyu Dong Department of Computer Science Ocean University of China Qingdao China Department of Statistics University of California Los Angeles USA Lotus Hill Research Institute Hubei China

ISBN: (纸本)9781424421749

This paper proposes a hybrid model for deformable template which combines alignable and non-alignable sketches. These sketches are subject to slight or considerable translations in different images. For slight translations, Wu et al proposed active basis model to capture them, where each sketch is allowed to shift in position and orientation. For larger translations of sketches, assumed that they follow the same distribution as sketches of natural image ensembles, which need not be explicitly modeled. But in fact, for a specified object class, the unaligned sketches follow a totally different distribution from those of natural images. We summarize these sketches by their means in the foreground mask. We treat the mean value in each direction as independent features and fit their marginal distributions on object ensemble and natural image ensemble using Gaussian distribution. The marginal distributions are combined with Active Basis into a joint probability ratio to distinguish foreground object from natural background. Experiments are conducted on 14 object classes, most of which show considerable improvement in ROC.

关键词： Deformable models Gaussian distribution Computer science Oceans Statistics Testing pursuit algorithms Dictionaries Object recognition Filters

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Improvement with Joint Rewards on Multi-agent Cooperative Reinforcement Learning

Improvement with Joint Rewards on Multi-agent Cooperative Re...

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International Conference on Computer Science and Software Engineering

作者： Pan Ying Li Dehua Institute for Pattern Recognition and Artificial Intelligence Huangzhong University of Science and Technology Wuhan China

Cooperation among agents is important for multi-agent system. In this paper, an improved cooperative reinforcement learning algorithm is proposed, which based on joint rewards to insure agents to learn cooperative behavior. Furthermore, a symmetry idea is included in the algorithm to reduce the states size of reinforcement learning. The experiment results show the efficiency and well convergence of the algorithm.

关键词： Pattern recognition Artificial intelligence Multiagent systems State-space methods Computer science Software engineering pursuit algorithms Testing Learning systems

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Low Bit Rate Image Coding Based Overcomplete Sparse Representation

Low Bit Rate Image Coding Based Overcomplete Sparse Represen...

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International Symposium on Intelligent Information Technology Application, IITA

作者： Chengzhi Deng Shengqian Wang Department of Company Science & Technology Nanchang Institute of Technology Nanchang China

A sparse image representation algorithm, named multi-atoms matching pursuit (MAMP), and its application to still image coding are proposed. The MAMP algorithm decomposes images sparsely by selecting several atoms at each iteration. The approximation performance of the MAMP algorithm is comparable with those of matching pursuit (MP). Meanwhile, the speeds of sparse decomposition in MAMP algorithm are greatly improved. A multiscale ridge let dictionary is designed to sparsely represent images. The coding performances are tested. Experimental results show the performances of this paper outperform the state of the art JPEG-2000 in terms of both PSNR and visual quality, especially at low bit rate.

关键词： Bit rate Image coding Matching pursuit algorithms pursuit algorithms Image representation Approximation algorithms Dictionaries Performance evaluation Testing PSNR

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Compressed sensing with sequential observations

Compressed sensing with sequential observations

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： D. M. Malioutov S. Sanghavi A. S. Willsky Massachusetts Institute of Technology Cambridge MA USA

Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of measurements. The results in the literature have focused on the asymptotics of how many samples are required and the probability of making an error for & fixed batch of samples. We investigate an alternative scenario where observations are available in sequence and can be stopped as soon as there is reasonable certainty of correct reconstruction. This approach does not require knowing how sparse is the signal, and allows reconstruction using the smallest number of samples. Central to our sequential approach is the stopping rule. For the random Gaussian ensemble we show that a simple stopping rule gives the absolute minimum number of observations required for exact recovery, with probability one. However, for other ensembles like Bernoulli or Fourier, this is no longer true, and the rule is modified to trade off delay in stopping and probability of error. We also consider near-sparse signals and describe how to estimate the reconstruction error from the sequence of solutions. This enables stopping once the error falls below a desired tolerance. Our sequential approach to compressed sensing involves a sequence of linear programs, and we outline how such a sequence can be solved efficiently.

关键词： Compressed sensing Decoding Delay pursuit algorithms Costs Signal analysis Guidelines Military computing Gaussian noise Noise measurement

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Multi-focus Image Fusion Based on Adaptive Features Matching

Multi-focus Image Fusion Based on Adaptive Features Matching

引用

International Conference on Computer Science and Software Engineering

作者： Bin Liao Jun Lu School of Electric and Electronical Engineering North China Electric Power University Beijing China

Multifocus fusion is an important technique to integrate focal information from a set of input registered images. In this effort, a novel image fusion algorithm based on adaptive features matching is presented. Given an over-complete Gabor dictionary, through orthogonal matching pursuit signal decomposition algorithm, each source image can be described by sparse combinations of these atoms which capture the main features in the images. Then the fused image can be reconstructed by merging the atoms information in source images together. The last experiment results show that our algorithm can achieve better fusion effect than traditional wavelet image fusion method and spatial frequency fusion method, whatever from subjective visual effect or objective metric.

关键词： Image fusion Matching pursuit algorithms Signal processing algorithms Dictionaries pursuit algorithms Frequency Pixel Merging Visual effects Iterative algorithms

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Iteratively reweighted algorithms for compressive sensing

Iteratively reweighted algorithms for compressive sensing

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rick Chartrand Wotao Yin Los Alamos National Laboratory USA Rice University USA

The theory of compressive sensing has shown that sparse signals can be reconstructed exactly from many fewer measurements than traditionally believed necessary. In [1], it was shown empirically that using lscr p minimization with p < 1 can do so with fewer measurements than with p = 1. In this paper we consider the use of iteratively reweighted algorithms for computing local minima of the nonconvex problem. In particular, a particular regularization strategy is found to greatly improve the ability of a reweighted least-squares algorithm to recover sparse signals, with exact recovery being observed for signals that are much less sparse than required by an unregularized version (such as FOCUSS, [2]). Improvements are also observed for the reweighted-lscr 1 approach of [3].

关键词： Iterative algorithms Image reconstruction Image coding pursuit algorithms Size measurement Laboratories Focusing Signal reconstruction Least squares methods Biomedical imaging

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