37911723
The Research of Complex System Modeling Based on Fractal Conception
45515067
Evaluation Mode Research on Particle Swarm Optimization Algorithm
14823691
MRF parameter estimation by MCMC method
Abstract Markov random field (MRF) modeling is a popular pattern analysis method and MRF parameter estimation plays an important role in MRF modeling. In this paper, a method based on Markov Chain Monte Carlo (MCMC) is proposed to estimate MRF parameters. Pseudo-likelihood is used to represent likelihood function and it gives a good estimation result.
15438049
DECF: A Coarse-Grained Data-Parallel Programming Framework for Seismic Processing
In this paper, we present a distributed execution control framework (DECF) for seismic processing. The DECF framework employs a coarse-grained data-parallel execution model within a cluster environment and achieves high throughput. The DECF schedules processing modules written as sequential programs to run simultaneously on multiple computers, or multiple CPU cores. The DECF hides the messy details of scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between processing modules.We also introduce a parallel implementation of 3D pre-stack kirchhoff time migration algorithm using MPI and share the practical lessons learned from the process of designing, implementing, and maintaining it, which are of great help to the future work of the DECF.
14307736
Positive Semidefinite Metric Learning with Boosting
The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed BOOSTMETRIC, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. BOOSTMETRIC is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices. BOOSTMETRIC thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting method is easy to implement, does not require tuning, and can accommodate various types of constraints. Experiments on various datasets show that the proposed algorithm compares favorably to those state-of-the-art methods in terms of classification accuracy and running time.
3194016
A Novel Model of Artificial Immune Network and Simulations on Its Dynamics
A novel model of artificial immune network is presented at first, and then a simulative research work is made on its dynamic behaviors. In this model, a B cell makes a key role that takes antigens in, so as to generate antibodies as its outputs. Under five different kinds of adjustment by suppressor T cells, number of antibodies will keep to a certain degree through influencing the B cell’s activation. On the other hand, with help T cells, different B cells could cooperate from each other, which makes the system’s dynamic behavior appear more complex, such as phenomena of limit cycle, chaos, etc. Simulative results show that limit cycle and chaos may exist simultaneously when four units are in connection, and the network’s characteristic has a close relationship with the intensity of suppressor T cell’s function.
15237813
Research on an Immune Mechanism Based Intelligent Spam Filter
With regard to the problem that the traditional antispam filter is usually incapable of recognizing unknown and mutational characters, a novel anti-spam filter based on immune mechanisms is proposed which employs multiple principles and mechanisms of the artificial immune system, such as the self tolerance, immune recognition, immune learning and immune memory. Through combining the idea of intelligent decision support system with the extension of classical rough set theory, a novel algorithm is provided for finding minimal attributes extraction and updating the attributes base. Simulation shows that this technique is able to reduce and improve the availability of the normal anti-spam systems.