Employing optimized combinations of one-class classifiers for automated currency validation

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution.     

基于模式间混淆关系自适应构建层次化分类器

为了解决层次化分类器的设计难点——子分类器的层属关系及其内部组成的确定,本文首先定义了模式间混淆关系,用于描述不同模式在判决域中的相互作用;进而提出了基于混淆矩阵度量模式间混淆关系的方法。设计并实现了多模式混淆关系分析机MPCRAM,将有师指派和无师自组两种常用的模式重组方法有机结合,遵循Fisher准则,自适应地产生层次化分类器结构。大量综合测试证实了该方法有效、实用,可显著提高分类器的识别性能和稳健性。     

Improving the performance of the BioHEL learning classifier system

The identification of significant attributes is of major importance to the performance of a variety of Learning Classifier Systems including the newly-emerged Bioinformatics-oriented Hierarchical Evolutionary Learning (BioHEL) algorithm. However, the BioHEL fails to deliver on a set of synthetic datasets which are the checkerboard data mixed with Gaussian noises due to the fact the significant attributes were not successfully recognised. To address this issue, a univariate Estimation of Distribution Algorithm (EDA) technique is introduced to BioHEL which primarily builds a probabilistic model upon the outcome of the generalization and specialization operations. The probabilistic model which estimates the significance of each attribute provides guidance for the exploration of the problem space. Experiment evaluations showed that the proposed BioHEL systems achieved comparable performance to the conventional one on a number of real-world small-scale datasets. Research efforts were also made on finding the optimal parameter for the traditional and proposed BioHEL systems.     

基于ARM的逻辑门电路手绘草图识别

针对电路草图识别主要在计算机上完成以及只能识别电路元件的问题,设计一个在ARM处理器和Linux系统上实现的逻辑门电路草图整图识别系统。采取在线识别的方式对触摸屏上输入的手绘笔画进行分段识别、组合、分类得到门电路的图元,按图元顺序建立图元列表,分割出逻辑门图元组合,重构门电路整图。实验证明该系统对逻辑门电路草图有很高的识别率。     

基于支持向量机的AdaBoost人脸检测方法

人脸的检测与识别技术因其巨大的应用价值及市场潜力,引起各方面的关注,已经成为计算机视觉领域的研究热点.介绍了一种基于支持向量机(SVM)的AdaBoost人脸检测方法.与原有的AdaBoost算法相比,AdaBoostSVM算法通过设置核参数σ的最小值,并自适应地调整σ值来解决AdaBoost算法分类器训练中的过学习问题.该方法降低了复杂性,增强了推广性.实验结果证明,对于人脸模型具有较好的检测效果,并且比单纯运用AdaBooet算法具有更高的正确检测率.     

Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers

The One-vs-One strategy is one of the most commonly used decomposition technique to overcome multi-class classification problems; this way, multi-class problems are divided into easier-to-solve binary classification problems considering pairs of classes from the original problem, which are then learned by independent base classifiers.The way of performing the division produces the so-called non-competence. This problem occurs whenever an instance is classified, since it is submitted to all the base classifiers although the outputs of some of them are not meaningful (they were not trained using the instances from the class of the instance to be classified). This issue may lead to erroneous classifications, because in spite of their incompetence, all classifiers' decisions are usually considered in the aggregation phase.In this paper, we propose a dynamic classifier selection strategy for One-vs-One scheme that tries to avoid the non-competent classifiers when their output is probably not of interest. We consider the neighborhood of each instance to decide whether a classifier may be competent or not. In order to verify the validity of the proposed method, we will carry out a thorough experimental study considering different base classifiers and comparing our proposal with the best performer state-of-the-art aggregation within each base classifier from the five Machine Learning paradigms selected. The findings drawn from the empirical analysis are supported by the appropriate statistical analysis.     

A learning classifier system for mazes with aliasing clones

Maze problems represent a simplified virtual model of the real environment and can be used for developing core algorithms of many real-world application related to the problem of navigation. Learning Classifier Systems (LCS) are the most widely used class of algorithms for reinforcement learning in mazes. However, LCSs best achievements in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons for failure. Moreover, there is a lack of knowledge of what makes a maze problem hard to solve by a learning agent. To overcome this restriction we try to improve our understanding of the nature and structure of maze environments. In this paper we describe a new LCS agent that has a simpler and more transparent performance mechanism. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability to Associative Perception, adopted from psychology. We then assess the new LCS with Associative Perception on an extensive set of mazes and analyse the results to discover which features of the environments play the most significant role in the learning process. We identify a particularly hard feature for learning in mazes, aliasing clones, which arise when groups of aliasing cells occur in similar patterns in different parts of the maze. We discuss the impact of aliasing clones and other types of aliasing on learning algorithms.     

鲁棒的多体印刷英文识别系统的实现

文章讨论了设计一个实用的多体英文识别系统中解决的主要问题。该系统能识别多达260种字体,包括斜体和黑体等字体,对训练集的识别率达到99%,对实际文本测试的错误率比TH-OCR2000低56%。文章详细阐述了文本行字切分,特征提取和分类器设计,以及后处理所使用的常用技术,对各种技术的特点进行了分析和比较,并提出了一些新的技术。文章对于OCR系统的设计具有一定的指导意义。     

一种基于神经网络和系统调用的异常入侵检测方法*

提出了一种利用多层BP神经网络建立进程正常运行轮廓的思想,基于1998年DARPA入侵检测系统评估的审计数据源,通过与K近邻分类、带(频率)门限的Stide(TStide)算法的性能比较表明,所提出的算法具有较高的检测率,同时维持了一个较低的误警率。     

基于数据挖掘方法的电子邮件过滤

电子邮件在给人们带来很多方便抽时,也产生了一个新的问题,即大量垃圾邮件的出现。邮件过滤就是从大量邮件中过滤出垃圾邮件,帮助用户寻找到所需要的有用邮件。本文介绍了一个基于数据挖掘方法的邮件过滤系统原型,给出了Bayes方法的几个基于概念,并重点讨论了要实现这个系统所需要处理的几个关键问题。