基于Gabor小波的人脸检测

提出了一种新的正面人脸检测算法。该方法组合了Gabor小波变换、输入图像的Gabor特征分析和Bayes分类器来进行正面人脸检测。对训练集的平均脸作Gabor小波变换得到40个投影向量；通过计算输入图像和这40个投影向量间的内积来提取图像的Gabor特征向量；训练Bayes分类器来进行正面人脸检测。实验结果表明，该算法的计算效率和检测精度均优于特征脸方法。     

基于贝叶斯最小错误率的一种新的指纹分割算法

指纹图像分割是自动指纹识别系统中非常重要的一步。本文介绍了一种新的基于贝叶斯最小错误率的指纹分割方法。首先,提出了一种新的基于方向信息的分割特征。然后,具体介绍了基于这种特征的分割算法。最后根据最小错误率的贝叶斯决策确定合适的阈值割除背景区域。这种方法在FVC2000指纹库中进行了测试,实验结果证明了它的有效性。     

贝叶斯理论在垃圾邮件过滤中的应用分析

电子邮件给我们的生活带来了极大的便利,但是许多无用的信息也随之而来。贝叶斯算法是一种基于内容的垃圾邮件过滤方法,文章首先给出了其应用原理,接着分析了其相比于其他过滤方法的优缺点,特别是其用于处理中文垃圾邮件时的不足之处。最后提出了一些改进措施及发展方向。     

基于多Agent的网络故障诊断系统

论文针对现有网络故障诊断系统的局限性,提出采用Agent技术解决网络故障的诊断方法。在分布式环境中,按照系统和网络的故障模式的不同特征和环境差异,可以利用不同的Agent进行诊断,Agent之间能够相互协作,互相交流信息和进行数据收集。该模型具有可扩充性,采用了状态检查,保证了Agent的自身安全。实验表明,利用该方法实现的系统在进行网络故障诊断时可以取得较好的效果。     

统计模式识别研究进展

研究了统计模式识别研究的主要新进展。介绍了统计模式识别的原理和方法。从类条件概率分布的估计、线性判别法、贝叶斯分类器、误差界以及新的模式识别模型等方面概述了近几年有关统计模式识别方面的研究进展。最后进行了评述。     

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.     

数据库信息分类中贝叶斯网络模型的应用

数据库信息分类中 ,朴素贝叶斯分类模型是一种简单而有效的分类方法 ,但它的属性独立性假设使其无法表达属性变量间存在的依赖关系 ,影响了它的分类性能 .而一般贝叶斯网络模型则由于能表达属性变量之间的依赖关系而越来越受到人们的重视 ,但一般贝叶斯网络分类模型结构的学习算法是一个NP完全问题 .本研究在一种简化的贝叶斯网络分类模型的基础上 ,利用其多项式时间复杂度的结构学习算法 ,将其应用于数据库信息分类 ,实现了学习效率和分类精度的一种折衷 .实验结果表明 ,这种分类方法有着比较高的数据库信息文本检索性能 .     

Using differential evolution for fine tuning naïve Bayesian classifiers and its application for text classification

The Naive Bayes (NB) learning algorithm is simple and effective in many domains including text classification. However, its performance depends on the accuracy of the estimated conditional probability terms. Sometimes these terms are hard to be accurately estimated especially when the training data is scarce. This work transforms the probability estimation problem into an optimization problem, and exploits three metaheuristic approaches to solve it. These approaches are Genetic Algorithms (GA), Simulated Annealing (SA), and Differential Evolution (DE). We also propose a novel DE algorithm that uses multi-parent mutation and crossover operations (MPDE) and three different methods to select the final solution. We create an initial population by manipulating the solution generated by a method used for fine tuning the NB. We evaluate the proposed methods by using their resulted solutions to build NB classifiers and compare their results with the results of obtained from classical NB and Fine-Tuning Naïve Bayesian (FTNB) algorithm, using 53 UCI benchmark data sets. We name these obtained classifiers NBGA, NBSA, NBDE, and NB-MPDE respectively. We also evaluate the performance NB-MPDE for text-classification using 18 text-classification data sets, and compare its results with the results of obtained from FTNB, BNB, and MNB. The experimental results show that using DE in general and the proposed MPDE algorithm in particular are more convenient for fine-tuning NB than all other methods, including the other two metaheuristic methods (GA, and SA). They also indicate that NB-MPDE achieves superiority over classical NB, FTNB, NBDE, NBGA, NBSA, MNB, and BNB.     

Gaussian mixture presentation of measurements for long-range radar tracking

In long-range radar tracking, the measurement uncertainty region has a thin and curved shape in Cartesian space due to the fact that the measurement is accurate in range but inaccurate in angle. Such a shape reflects grievous measurement nonlinearity, which can lead to inconsistency in tracking performance and significant tracking errors in traditional nonlinear filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we propose a modified version of the Gaussian Mixture Measurement-Integrated Track Splitting (GMM-ITS) filter to deal with the nonlinearity of measurements in long-range radar tracking. Not only is the state probability density function (pdf) approximated by a set of Gaussian track components, but the likelihood function (LF) is approximated by several Gaussian measurement components. In this way, both the state pdf and LF in the proposed filter have more accurate approximation than traditional filters that approximate measurements using just one Gaussian distribution. Simulation experiments show that the proposed filter can successfully avoid the inconsistency problem and also obtain high tracking accuracy in both 2-D (with range-angle measurements) and 3-D (with range-direction-cosine measurements) long-range radar tracking.     

An analysis of the transition proportion for binarization in handwritten historical documents

In this paper, we will present a mathematical analysis of the transition proportion for the normal threshold (NorT) based on the transition method. The transition proportion is a parameter of NorT which plays an important role in the theoretical development of NorT. We will study the mathematical forms of the quadratic equation from which NorT is computed. Through this analysis, we will describe how the transition proportion affects NorT. Then, we will prove that NorT is robust to inaccurate estimations of the transition proportion. Furthermore, our analysis extends to thresholding methods that rely on Bayes rule, and it also gives the mathematical bases for potential applications of the transition proportion as a feature to estimate stroke width and detect regions of interest. In the majority of our experiments, we used a database composed of small images that were extracted from DIBCO 2009 and H-DIBCO 2010 benchmarks. However, we also report evaluations using the original (H-)DIBCO?s benchmarks.