Linear reconstruction measure steered nearest neighbor classification framework

The linear reconstruction measure (LRM), which determines the nearest neighbors of the query sample in all known training samples by sorting the minimum L₂-norm error linear reconstruction coefficients, is introduced in this paper. The intuitive interpretation and mathematical proofs are presented to reveal the efficient working mechanism of LRM. Through analyzing the physical meaning of coefficients and regularization items, we find that LRM provides more useful information and advantages than the conventional similarity measure model which calculates the distance between two entities (i.e. conventional point-to-point, C-PtP). Inspired by the advantages of LRM, the linear reconstruction measure steered nearest neighbor classification framework (LRM-NNCF) is designed with eight classifiers according to different decision rules and models of LRM. Evaluation on several face databases and the experimental results demonstrate that these proposed classifiers can achieve greater performance than the C-PtP based 1-NNs and competitive recognition accuracy and robustness compared with the state-of-the-art classifiers.     

Feature selection by interactive clustering

The results of experiments with a novel criterion for absolute non-parametric feature selection are reported. The basic idea of the new technique involves the use of computer graphics and the human pattern recognition ability to interactively choose a number of features, this number not being necessarily determined in advance, from a larger set of measurements. The triangulation method, recently proposed in the cluster analysis literature for mapping points from l-space to 2-space, is used to yield a simple and efficient algorithm for feature selection by interactive clustering. It is shown that a subset of features can thus be chosen which allows a significant reduction in storage and time while still keeping the probability of error in classification within reasonable bounds.     

A Tale of Two Classifier Systems

This paper describes two classifier systems that learn. These are rule-based systems that use genetic algorithms, which are based on an analogy with natural selection and genetics, as their principal learning mechanism, and an economic model as their principal mechanism for apportioning credit. CFS-C is a domain-independent learning system that has been widely tested on serial computers. CFS is a parallel implementation of CFS-C that makes full use of the inherent parallelism of classifier systems and genetic algorithms, and that allows the exploration of large-scale tasks that were formerly impractical. As with other approaches to learning, classifier systems in their current form work well for moderately-sized tasks but break down for larger tasks. In order to shed light on this issue, we present several empirical studies of known issues in classifier systems, including the effects of population size, the actual contribution of genetic algorithms, the use of rule chaining in solving higher-order tasks, and issues of task representation and dynamic population convergence. We conclude with a discussion of some major unresolved issues in learning classifier systems and some possible approaches to making them more effective on complex tasks.     

The relative neighborhood graph for mixed feature variables

The rectangular influence graph (RIG) is presented as an extension of the relative neighborhood graph (RNG). The RNG is an efficient tool for analyzing the clustering of multidimensional feature vectors when all the features are quantitative. The RIG is a similar tool that can accommodate feature vectors some of whose components are qualitative. We show that the RIG is a superset of the Gabriel graph with respect to any Minkowski metric.As tools to analyze interclass structure, the interclass RIG (IRIG) and the mutual neighborhood graph (MNG) are presented. These graphs can be used to reduce the training set in the design of piecewise linear classifiers. The MNG leads also to a sufficient condition for the linear separability between classes.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

基于依存句法分析与分类器融合的触发词抽取方法

事件检测与分类是事件抽取的关键环节,触发词抽取是完成事件检测与分类的主流方法。提出了一种事件触发词抽取方法,该方法针对单一触发词抽取方法没有充分利用依存句法分析信息且召回率不高的问题,通过综合利用依存句法分析信息和其他信息抽取触发词-实体描述对的方法来提高触发词抽取的召回率,然后将触发词-实体描述对抽取结果与单一触发词抽取结果相融合以避免召回率提高所带来的准确率下降问题。在ACE2005中文语料上进行实验,该方法在事件检测与分类任务中取得较好效果,F值分别达到了69.0%和66.2%。     

网络攻击特征库的优化设计与实现

随着网络入侵检测系统的广泛使用,作为系统核心部件的网络攻击特征库对网络入侵检测系统性能的影响越来越大。论文根据网络攻击特征库的特点对其进行了优化设计,将网络攻击特征库分解为入侵行为特征描述库和入侵确认库两个核心库,通过实验证明该设计方案可显著提高网络入侵检测系统的性能。     

一种简单有效的多分类器综合方法

针对小字符集脱机手写体汉字识别中的多分类器集成问题，提出了一种简单有效的综合方法，实验表明综合后系统的识别率明显高于单个分类器的识别率。     

人工神经网络和模式识别

80年代重新兴起的人工神经网络已成为世界人工智能研究的热门课题之一。本文介绍了人工神经网络的一般结构及其算法,着重介绍人工神经网络在模式识别方面的作用及用作模式识别的人工神经网络分类器。希望能给读者了解这一当今世界科学前沿热门课题起一介绍作用。     

A divide-and-conquer method for multi-net classifiers

Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a pattern classification multi-net system based on both supervised and unsupervised learning. Following the ‘divide-and-conquer’ framework, the input space is partitioned into overlapping subspaces and neural networks are subsequently used to solve the respective classification subtasks. Finally, the outputs of individual classifiers are appropriately combined to obtain the final classification decision. Two clustering methods have been applied for input space partitioning and two schemes have been considered for combining the outputs of the multiple classifiers. Experiments on well-known data sets indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel). ID="A1"Correspondance and offprint requests to: D. Frosyniotis, National Technical University of Athens, Department of Electrical and Computer Engineering, Zographou 157 73, Athens, Greece. E-mail: andreas@cs.ntua.gr