In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate pr...In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.展开更多
To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M a...To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.展开更多
Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qu...Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.展开更多
Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression num...Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression numerical algorithm in the best m -term approximation with regard to tensor product wavelet-type basis is pro-posed. The algorithm provides the asymptotically optimal approximation for the class of periodic functions with mixed Besov smoothness in the L q norm. Moreover, it depends only on the expansion of function f by tensor pro-duct wavelet-type basis, but neither on q nor on any special features of f.展开更多
This paper presents an algorithm for computing a linear recurrence system R(n, m) of order m for n equations on MIMD parallel system. This algorithm is not only easy to be programmed on a parallel computer system, but...This paper presents an algorithm for computing a linear recurrence system R(n, m) of order m for n equations on MIMD parallel system. This algorithm is not only easy to be programmed on a parallel computer system, but also reduces the data-waiting time due to compute-ahead strategy. The paper analyses how to achieve maximal load balancing when the algorithm is implemented on MIMD parallel system. By the end of the paper, an analysis on the speedup and parallel efficiency are given. The results indicate that the new parallel elimination algorithm has great improvement compared with the old ones.展开更多
In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the gen...In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the generalized resolvent operator technique associated with the (A,η,m)-monotone operators, the approximation solvability of the operator equation problems and the convergence of iterative sequences generated by the algorithm are discussed. Our results improve and generalize the corresponding results in the literature.展开更多
This paper offers a positive research result of TIP before 16 strong earthquakes in North and Southwest China and their nearby areas since 1979 by using improved algorithm M8.The result showed that 14 of them were det...This paper offers a positive research result of TIP before 16 strong earthquakes in North and Southwest China and their nearby areas since 1979 by using improved algorithm M8.The result showed that 14 of them were determined to occur within the times of increased probability.TIP precaution occupies about 37% of the total space-time domain.That means we have made quite good results of intermediate-term prediction of strong earthquakes.So the method could be used as one of the useful means of the intermediate-term prediction of strong earthquakes.展开更多
A Leverrier-like algorithm is presented which allows the computation of the transfer function of a linear regular system from its m-D state-space description,without inverting a multivariable polynomial matrix. This a...A Leverrier-like algorithm is presented which allows the computation of the transfer function of a linear regular system from its m-D state-space description,without inverting a multivariable polynomial matrix. This algorithm is an extension of the classic Leverrier's algorithm for 1-D system and it reduces the computational cost.ra-D Cayley-Hamilton theorem is also shown by the algorithm.展开更多
A direct algorithm is proposed by which one can distinguish whether a matrix is an M-matrix (or H-matrix) or not quickly and effectively. Numerical examples show that it is effective and convincible to distinguish M-m...A direct algorithm is proposed by which one can distinguish whether a matrix is an M-matrix (or H-matrix) or not quickly and effectively. Numerical examples show that it is effective and convincible to distinguish M-matrix (or H-matrix) by using the algorithm.展开更多
We consider the inverse problem of finding cavities within some object from electrostatic measurements on the boundary. By a cavity we understand any object with a different electrical conductivity from the background...We consider the inverse problem of finding cavities within some object from electrostatic measurements on the boundary. By a cavity we understand any object with a different electrical conductivity from the background material of the body. We give an algorithm for solving this inverse problem based on the output nonlinear least-square formulation and the regularized Newton-type iteration. In particular, we present a number of numerical results to highlight the potential and the limitations of this method.展开更多
QR Decompositon with an M-algorithm(QRD-M) has good performance with low complexity,which is considered as a promising technique in Multiple-Input Multiple-Output(MIMO) detections.This paper presented a simplified QRD...QR Decompositon with an M-algorithm(QRD-M) has good performance with low complexity,which is considered as a promising technique in Multiple-Input Multiple-Output(MIMO) detections.This paper presented a simplified QRD-M algorithm for MIMO Orthogonal Frequency Division Multiplexing(MIMO-OFDM) systems.In the proposed scheme,each surviving path is expanded only to partial branches in order to carry out a limited tree search.The nodes are expanded on demand and sorted in a distributed manner,based on the novel expansion scheme which can pre-determine the children's ascending order by their local distances.Consequently,the proposed scheme can significantly decrease the complexity compared with conventional QRD-M algorithm.Hence,it is especially attractive to VLSI implementation of the high-throughput MIMO-OFDM systems.Simulation results prove that the proposed scheme can achieve a performance very close to the conventional QRD-M algorithm.展开更多
Let A be m by n matrix, M and N be positive definite matrices of order in and n respectively. This paper presents an efficient method for computing (M-N) singular value decomposition((M-N) SVD) of A on a cube connecte...Let A be m by n matrix, M and N be positive definite matrices of order in and n respectively. This paper presents an efficient method for computing (M-N) singular value decomposition((M-N) SVD) of A on a cube connected single instruction stream-multiple data stream(SIMD) parallel computer. This method is based on a one-sided orthogonalization algorithm due to Hestenes. On the cube connected SIMD parallel computer with o(n) processors, the (M -- N) SVD of a matrix A requires a computation time of o(m3 log m/n).展开更多
An adaptive MIMO detection algorithm for LTE-A system which is based on sphere detection is proposed in this paper. The proposed algorithm uses M-algorithm for reference to remove unreliable constellation candidates b...An adaptive MIMO detection algorithm for LTE-A system which is based on sphere detection is proposed in this paper. The proposed algorithm uses M-algorithm for reference to remove unreliable constellation candidates before search, and the number of constellation reservation is adaptively adjusted according to SNR. Simulations of LTE-A downlink show that the BER performance of the proposed detection algorithm is nearly the same as maximum likelihood (ML) detection algorithm. However, the complexity is reduced by about 30% compared with full constellation sphere detection.展开更多
文摘In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.
文摘To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.
基金supported by the National Natural Science Foundation of China(61573017 61703425)+2 种基金the Aeronautical Science Fund(20175796014)the Shaanxi Province Natural Science Foundation Research Project(2016JQ6062 2017JM6062)
文摘Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.
基金Supported by National Natural Science Foundation of China (No. 60872161, 10501026, 60675010 and 10626029)Natural Science Foundation of Tianjin (No. 08JCYBJC09600)China Postdoctoral Science Foundation ( No. 20070420708).
文摘Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression numerical algorithm in the best m -term approximation with regard to tensor product wavelet-type basis is pro-posed. The algorithm provides the asymptotically optimal approximation for the class of periodic functions with mixed Besov smoothness in the L q norm. Moreover, it depends only on the expansion of function f by tensor pro-duct wavelet-type basis, but neither on q nor on any special features of f.
文摘This paper presents an algorithm for computing a linear recurrence system R(n, m) of order m for n equations on MIMD parallel system. This algorithm is not only easy to be programmed on a parallel computer system, but also reduces the data-waiting time due to compute-ahead strategy. The paper analyses how to achieve maximal load balancing when the algorithm is implemented on MIMD parallel system. By the end of the paper, an analysis on the speedup and parallel efficiency are given. The results indicate that the new parallel elimination algorithm has great improvement compared with the old ones.
文摘In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the generalized resolvent operator technique associated with the (A,η,m)-monotone operators, the approximation solvability of the operator equation problems and the convergence of iterative sequences generated by the algorithm are discussed. Our results improve and generalize the corresponding results in the literature.
基金This project was sponsored by the National Science Foundation, China
文摘This paper offers a positive research result of TIP before 16 strong earthquakes in North and Southwest China and their nearby areas since 1979 by using improved algorithm M8.The result showed that 14 of them were determined to occur within the times of increased probability.TIP precaution occupies about 37% of the total space-time domain.That means we have made quite good results of intermediate-term prediction of strong earthquakes.So the method could be used as one of the useful means of the intermediate-term prediction of strong earthquakes.
基金Supported by NSFC Project 19971057, Science and Technology Foundation of Shanhai Higher Eduction Project 03DZ04, and The Special Funds for Major Specialities of Shanghai Eduction Committee. Partly supported by Chinese Scholarship Council.
文摘A Leverrier-like algorithm is presented which allows the computation of the transfer function of a linear regular system from its m-D state-space description,without inverting a multivariable polynomial matrix. This algorithm is an extension of the classic Leverrier's algorithm for 1-D system and it reduces the computational cost.ra-D Cayley-Hamilton theorem is also shown by the algorithm.
基金Foundation item: This work is supported by the Science Foundations of the Education Department of Yunnan Province (03Z169A)the Science Foundatons of Yunnan University (2003Z013B).
文摘A direct algorithm is proposed by which one can distinguish whether a matrix is an M-matrix (or H-matrix) or not quickly and effectively. Numerical examples show that it is effective and convincible to distinguish M-matrix (or H-matrix) by using the algorithm.
文摘We consider the inverse problem of finding cavities within some object from electrostatic measurements on the boundary. By a cavity we understand any object with a different electrical conductivity from the background material of the body. We give an algorithm for solving this inverse problem based on the output nonlinear least-square formulation and the regularized Newton-type iteration. In particular, we present a number of numerical results to highlight the potential and the limitations of this method.
基金Supported by the ‘Transformation of Scientific and Technological Achievements Project’ funded by the Department of Science and Technology of Jiangsu province,P.R. China (BA2006076)
文摘QR Decompositon with an M-algorithm(QRD-M) has good performance with low complexity,which is considered as a promising technique in Multiple-Input Multiple-Output(MIMO) detections.This paper presented a simplified QRD-M algorithm for MIMO Orthogonal Frequency Division Multiplexing(MIMO-OFDM) systems.In the proposed scheme,each surviving path is expanded only to partial branches in order to carry out a limited tree search.The nodes are expanded on demand and sorted in a distributed manner,based on the novel expansion scheme which can pre-determine the children's ascending order by their local distances.Consequently,the proposed scheme can significantly decrease the complexity compared with conventional QRD-M algorithm.Hence,it is especially attractive to VLSI implementation of the high-throughput MIMO-OFDM systems.Simulation results prove that the proposed scheme can achieve a performance very close to the conventional QRD-M algorithm.
文摘Let A be m by n matrix, M and N be positive definite matrices of order in and n respectively. This paper presents an efficient method for computing (M-N) singular value decomposition((M-N) SVD) of A on a cube connected single instruction stream-multiple data stream(SIMD) parallel computer. This method is based on a one-sided orthogonalization algorithm due to Hestenes. On the cube connected SIMD parallel computer with o(n) processors, the (M -- N) SVD of a matrix A requires a computation time of o(m3 log m/n).
文摘An adaptive MIMO detection algorithm for LTE-A system which is based on sphere detection is proposed in this paper. The proposed algorithm uses M-algorithm for reference to remove unreliable constellation candidates before search, and the number of constellation reservation is adaptively adjusted according to SNR. Simulations of LTE-A downlink show that the BER performance of the proposed detection algorithm is nearly the same as maximum likelihood (ML) detection algorithm. However, the complexity is reduced by about 30% compared with full constellation sphere detection.
