Uncertainty,belief, and probability1

We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. In addition to removing the requirement that every event be assigned a probability, our approach circumvents other criticisms of probability-based approaches to uncertainty. For example, the measure of belief in an event turns out to be represented by an interval (defined by the inner and outer measures), rather than by a single number. Further, this approach allows us to assign a belief (inner measure) to an event E without committing to a belief about its negation -E (since the inner measure of an event plus the inner measure of its negation is not necessarily one). Interestingly enough, inner measures induced by probability measures turn out to correspond in a precise sense to Dempster-Shafer belief functions. Hence, in addition to providing promising new conceptual tools for dealing with uncertainty, our approach shows that a key part of the important Dempster-Shafer theory of evidence is firmly rooted in classical probability theory. Cet article présente une nouvelle approche probabiliste en ce qui concerne le traitement de l'incertitude; celle-ci est basée sur l'observation que la théorie des probabilityés n'exige pas qu'une probabilityé soit assignée à chaque événement. Dans le cas d'un événement non mesurable (un événement pour lequel on n'assigne aucune probabilityé), nous ne pouvons discuter que de la mesure intérieure et de la mesure extérieure de l'évenément. En plus d'éliminer la nécessité d'assigner une probabilityéà l'événement, cette nouvelle approche apporte une réponse aux autres critiques des approches à l'incertitude basées sur des probabilityés. Par exemple, la mesure de croyance dans un événement est représentée par un intervalle (défini par la mesure intérieure et extérieure) plutǒt que par un nombre unique. De plus, cette approche nous permet d'assigner une croyance (mesure intérieure) à un événement E sans se compromettre vers une croyance à propos de sa négation -E (puisque la mesure intérieure d'un événement et la mesure intérieure de sa négation ne sont pas nécessairement une seule et unique mesure). II est intéressant de noter que les mesures intérieures qui résultent des mesures de probabilityé correspondent d'une manière précise aux fonctions de croyance de Dempster-Shafer. En plus de constituer un nouvel outil conceptuel prometteur dans le traitement de l'incertitude, cette approche démontre qu'une partie importante de la théorie de l'évidence de Dempster-Shafer est fermement ancrée dans la theorie classique des probabilityés.     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Molecular simulation of phase coexistence: Finite-size effects and determination of critical parameters for two- and three-dimensional Lennard-Jones fluids

Abstact The subject of this paper is the investigation of finite-size effects and the determination of critical parameters for a class of truncated Lennard-Jones potentials. Despite significant recent progress in our ability to model phase equilibria in multicomponent mixtures from direct molecular simulations, the accurate determination of critical parameters remains a difficult problem. Gibbs ensemble Monte Carlo simulations with systems of controlled linear system size are used to obtain the phase behavior in the near-critical region for two- and three dimensional Lennard-Jones fluids with reduced cutoff radii of 3, 3.5, and 5. For the two-dimensional systems, crossover of the effective exponent for the width of the coexistence curve from mean field ( = 1/2 in the immediate vicinity of the critical point to Ising-like (= 1/8) farther away is observed. Critical parameters determined by fitting the data that follow Ising-like behavior are in good agreement with literature values obtained with finite-size scaling methods. For the three-dimensional systems, no crossover to mean field-type behavior was apparent. Extrapolated results for the critical parameters are consistent with literature estimates for similar fluids. For both two- and three-dimensional fluids, system size effects on the coexistence curves away from the critical point are small, normally within simulation statistical uncertainties.Invited paper presented at the Twelfth Symposium on Thermophysical Properties, June 19–24, 1994, Boulder. Colorado, U.S.A.     

基于Rough集方法的RS-MAXNET网络分类器模型

Rough集（RS）理论中如何补偿或拓展Rs约简的局部决策规则使之更加接近全局规则是一个重要的议题。本文提出RS与MAXNET网络结合拓展建立盼MAXNET全局网络分类器模型。基于上述模型进行的脱机手写模式识别算法说明了其有效性。     

基于HOG及在线多实例学习的目标跟踪算法

传统的基于色彩直方图或空间色彩直方图的跟踪算法在跟踪目标出现尺度变化的复杂条件下,因无法显著区分颜色相近的目标和背景,不能得到准确跟踪结果.提出基于HOG及在线多实例学习的目标跟踪算法.此算法采用HOG特征值提取方式,结合在线多实例学习技术,对目标远离场景、平移、旋转、遮挡等情况进行跟踪.实验结果表明,该算法能够对各种复杂情况下的动态目标进行有效跟踪,具有良好的鲁棒性和准确性.     

基于集成 PU 学习数据流分类的入侵检测方法

入侵检测问题可以模型化为数据流分类问题,传统的数据流分类算法需要标注大量的训练样本,代价昂贵,降低了相关算法的实用性。在PU学习算法中,仅需标注部分正例样本就可以构造分类器。对此本文提出一种动态的集成PU学习数据流分类的入侵检测方法,只需要人工标注少量的正例样本,就可以构造数据流分类器。在人工数据集和真实数据集上的实验表明,该方法具有较好的分类性能,在处理偏斜数据流上优于三种PU 学习分类方法,并具有较高的入侵检测率。     

经典分子动力学模拟的主要技术

综述了分子动力学模拟的基本原理、发展过程及主要应用,介绍了原子间势函数的发展及势参数的确定,给出了分子动力学模拟中相关的有限差分算法、初始条件及边界条件的选取、平衡态系综及其调控、感兴趣量的提取及主要过程。最后还指出了分子动力学模拟方法本身进一步的研究方向。     

一种基于信息复用的Boosting瀑布型分类器高效训练方法

构建一个基于Boosting的瀑布型分类器往往要花费巨大的计算代价,该文提出一种改进的Boosting瀑布型分类器训练算法,利用不同层级分类器之间的共享信息复用来提高训练速度。该文提出的瀑布型分类器训练方法在两个层次上复用已获得的层级分类器中的信息。在分类器层次上,前一个层级分类器在新的层级分类器训练过程中成为第1个特征,并在当前层级分类器的训练样本集上进行适应性学习;在特征层次上,每一级分类器都直接保留所有前面层级分类器已经学到的特征,并重新在当前层级分类器的训练样本集上进行适应性学习。最后,再通过学习新的弱分类器,加入新特征。在正面人脸检测问题上的实验表明,提出的新算法相比传统方法能够大幅提高Boosting瀑布型分类器的训练速度,提高幅度近10倍,瀑布型分类器的训练时间从将近3天降到了8个小时。     

A combined nonparametric approach to feature selection and binary decision tree design

An efficient procedure which integrates feature selection and binary decision tree construction is presented. The nonparametric approach is based on the Kolmogorov-Smirnov criterion which yields an optimal classification decision at each node. By combining the feature selection with the design of the classifier, only the most informative features are retained for classification.