Numeric mapping and learnability of naive bayes

In data-mining applications, it is common to transform (or map) nominal attributes into numeric ones in order to apply a specific model. However, a nominal attribute has typically no specific order in its values and no geometric meaning. An interesting issue is, does such a transformation change the property of a nominal function? How do you measure the geometric complexity of a nominal function independent of the mapping? This paper discusses the issue of converting a nominal function into a numeric one. We propose a three-layer measure for the geometric linearity of a nominal function and explore the geometric property of a nominal function independent of the mapping. Naive Bayes is one of the most efficient and effective inductive-learning algorithms for data mining. It is well known that Naive Bayes is linear in the binary domain; that is, it can learn only linearly separable functions. We show that Naive Bayes is actually nonlinear in the nominal domain, a general case of the binary domain, by exploring the geometric property of Naive Bayes. We investigate the geometric property of Naive Bayes based on the three-layer linearity measure that we propose. Our work helps researchers to understand the influence of numeric mapping on the property of a nominal function, and how numeric mapping affects the learnability of Naive Bayes.     

基于贝叶斯方法的计算机动态取证

针对目前计算机取证技术的真实性,时效性不足等问题,提出了一种基于朴素贝叶斯的计算机动态取证方法,该方法结合了贝叶斯网络在分类算法中的优势,能够对网络攻击行为进行及时准确地识别.在此基础上,设计了一个动态取证的原型系统,利用Agent节点自动完成取证任务,并采用了安全有效的方式对证据进行完整保存.实验结果表明,该系统对入侵行为有很高的检测率,能实时取到真实有效的电子证据,并具有一定的容错能力.     

The nearest neighbor and the bayes error rates

The (k, l) nearest neighbor method of pattern classification is compared to the Bayes method. If the two acceptance rates are equal then the asymptotic error rates satisfy the inequalities Ek,l + 1 ? E*(?) ? Ek,l dE*(?), where d is a function of k, l, and the number of pattern classes, and ? is the reject threshold for the Bayes method. An explicit expression for d is given which is optimal in the sense that for some probability distributions Ek,l and dE* (?) are equal.     

基于贝叶斯方法的配置管理研究

软件配置管理是一套规范、高效的软件开发管理方法,它能提供工作空间管理、并行开发支持、过程管理、权限控制、变更管理等一系列的管理能力,是提高软件质量的重要手段。设计了一个基于贝叶斯的软件配置管理模型,并详细阐述了该模型的核心技术和关键机制。     

Decision-directed multivariate empirical bayes classification with nonstationary priors

A decision-directed learning strategy is presented to recursively estimate (i.e., track) the time-varying a priori distribution for a multivariate empirical Bayes adaptive classification rule. The problem is formulated by modeling the prior distribution as a finite-state vector Markov chain and using past decisions to estimate the time evolution of the state of this chain. The solution is obtained by implementing an exact recursive nonlinear estimator for the rate vector of a multivariate discrete-time point process representing the decisions. This estimator obtains the Doob decomposition of the decision process with respect to the a-field generated by all past decisions and corresponds to the nonlinear least squares estimate of the prior distribution. Monte Carlo simulation results are provided to assess the performance of the estimator.     

A general bayes rule and its application to nonlinear estimation

This paper consists of two main results, a general Bayes rule, and a general Bucy representation theorem. The general Bayes rule is a natural generalization of the elementary Bayes rule:

$\text{P(A}\text{B)}\text{P(A)}\text{=}\text{P(B}\text{A)}\text{P(B)}$

. The general Bucy representation theorem plays a central role in nonlinear estimation theory as does the Bucy theorem in nonlinear filtering. A simple and direct proof of the general Bucy representation theorem is obtained by the application of the general Bayes rule.     

Matrix variational method

A general formalism is described for the matrix variational method, applicable to multichannel electron scattering by a many-electron atom, for energies below the first ionization threshold. Physical interpretation of the method and its relationship to others (close-coupling and polarized orbital methods) are discussed. Recent computations by this method of elastic and inelastic electron scattering are reviewed.     

基于改进朴素贝叶斯分类器的康复训练行为识别方法

为提高康复训练中行为的识别率,对康复训练行为识别进行研究。首先采用Kinect传感器提取人体骨骼坐标信息,定义运动特征分类集合,完成朴素贝叶斯分类器设计;然后改进康复训练动作识别阈值选择机制提升识别率。改进前后对比实验证明该方法快速简洁,取得了较理想的识别效果。     

Control of selective perception using bayes nets and decision theory

A selective vision system sequentially collects evidence to answer a specific question with a desired level of confidence. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decision making are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure (e.g., part-of and adjacent relationships), and also uses information from a scene instance gathered during analysis. The TEA-1 selective vision system uses Bayes nets for representation, benefit-cost analysis for control of visual and nonvisual actions; and its data structures and decision-making algorithms provide a general, reusable framework. TEA-1 solves the T-world problem, an abstraction of a large set of scene domains and tasks. Some factors that affect the success of selective perception are analyzed by using TEA-1 to solve ensembles of randomly produced, simulated T-world problems. Experimental results with a real-world T-world problem, dinner table scenes, are also presented.     

基于改进Nave Bayes的垃圾邮件过滤模型研究

分析了目前在垃圾邮件过滤中广泛应用的NaveBayes过滤模型(NBF),指出了期望交叉熵(ECE)特征词选取方法的不足。提出了改进的NaveBayes垃圾邮件过滤模型(A-NBF),用改进的期望交叉熵(AECE)选取垃圾邮件特征词,并在邮件分类过程中对特征词进行加权,从而提高对垃圾邮件过滤的精度。实验结果可以看出A-NBF比NBF在过滤精度方面有明显的提高。     

On bayes risk consistent pattern recognition procedures in a quasi-stationary environment

Van Ryzin and Greblicki showed that pattern recognition procedures derived from orthogonal series estimates of a probability density function are Bayes risk consistent. In this note it is proved that these procedures do not lose-under some additional conditions-their asymptotic properties even if the random environment is nonstationary.     

Approximations of bayes and minimax risks and the least favorable distribution

In this paper we present a method for approximating the risks and Bayes risk associated with a Bayes decision procedure. Additionally, our method leads to approximating the least favorable distribution and the risk associated with the minimax decision procedure. We assume two states of nature (or classes of patterns) and multivariate probability density functions. Taylor series expansions are used, and an nth-order polynomial equation derived from such expansions provides an approximation to one of the least favorable probabilities. An application to a normally distributed random vector of observables is presented with numerical comparisons. The method can be generalized to cases having more than two states of nature by using Taylor series expansions in several variables.     

Grid based variational approximations

Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alternatives to Monte Carlo methods. Unfortunately, unlike Monte Carlo methods, variational approximations cannot, in general, be made to be arbitrarily accurate. This paper develops grid-based variational approximations which endeavor to approximate marginal posterior densities in a spirit similar to the Integrated Nested Laplace Approximation (INLA) of Rue et al. (2009) but which may be applied in situations where INLA cannot be used. The method can greatly increase the accuracy of a base variational approximation, although not in general to arbitrary accuracy. The methodology developed is at least reasonably accurate on all of the examples considered in the paper.     

Increasing the accuracy of incremental naive bayes classifier using instance based learning

Along with the increase of data and information, incremental learning ability turns out to be more and more important for machine learning approaches. The online algorithms try not to remember irrelevant information instead of synthesizing all available information (as opposed to classic batch learning algorithms). In this study, we attempted to increase the prediction accuracy of an incremental version of Naive Bayes model by integrating instance based learning. We performed a large-scale comparison of the proposed method with other state-of-the-art algorithms on several datasets and the proposed method produce better accuracy in most cases.     

Patch-based variational image approximation

Vector graphic gives us a new solution to the representation of raster images. Among many types of vectorized representations, the most popular is mesh representation, which inherits the benefits of vector graphics. Inspired by mesh, we propose a novel patch-based representation for raster images, in which pixels are partitioned into regions, and pixels belonging to the same region are converted into a 3D point cloud and approximated by a 3D planar patch with proper boundaries in a variational way. The resulting patches are then encoded via a half-edge structure for storage. The key point is that the vertices of boundaries are not located on the very positions of sample points, i.e. converted pixels, but dependent on the optimal position of the patch, which theoretically reduces the fitting errors. Experiments show that our algorithm produces better results.     

Asynchronous variational contact mechanics

An asynchronous, variational method for simulating elastica in complex contact and impact scenarios is developed. Asynchronous variational integrators [1] (AVIs) are extended to handle contact forces by associating different time steps to forces instead of to spatial elements. By discretizing a barrier potential by an infinite sum of nested quadratic potentials, these extended AVIs are used to resolve contact while obeying momentum- and energy-conservation laws. A series of two- and three-dimensional examples illustrate the robustness and good energy behavior of the method.     

基于朴素贝叶斯算法的社交网络数据挖掘技术研究

近年来,随着互联网技术飞速发展与普及,大量社交网络平台迅速崛起。社交网络平台拉近了日常人际关系,提供了便捷的信息通讯交流通道。同时,针对社交网络平台数据挖掘的技术研究成为不可缺少的网络数据研究领域一部分。现有社交网络数据挖掘技术所采用的传统数据挖掘算法与数据分离模式,存在大数据多元特征条件下,数据挖掘准确度降低、挖掘分类逻辑混乱等现象。针对问题产生根源,提出基于朴素贝叶斯算法的社交网络数据挖掘技术研究。采用基于朴素贝叶斯算法设计的PCIE-FN社交网络数据挖掘平台进行全面化的深入性解决。通过实验证明,提出的基于朴素贝叶斯算法的社交网络数据挖掘技术研究,各项数据满足社交网络数据挖掘日常应用要求。     

模糊Bayes 理论在遥感影像变化检测中的应用

针对传统基于Bayes 决策规则的遥感影像变化检测方法中参数估计的不足以及分类过程中的硬划分问题,采用动态更新变化和未变化两类像元模糊子集的方法,实现对两类像元模糊子集中参数的动态更新,利用估计参数获得各子集的后验概率函数,再将后验概率函数转化为模糊子集的模糊隶属函数,从而获得各子集的指标函数,根据指标函数对影像中未分类的像元值进行判断,实现遥感影像的变化区域提取。实验结果表明：与现有的基于Bayes 决策规则的遥感影像变化检测方法及ERDAS 软件生成结果相比,提出的方法具有更好的变化检测精度。     

一种基于贝叶斯分类与机读词典的多义词排歧方法

一词多义是自然语言中普遍存在的现象,词义排歧的成功率是衡量机器翻译、信息检索、文本分类等自然语言处理软件性能的重要指标。提出了一种基于贝叶斯分类与机读词典的多义词排歧方法,通过小规模语料库的训练和歧义词在机读词典中的语义定义来完成歧义的消除。实验表明：基于贝叶斯分类与机读词典的多义词排歧算法在标注语料库规模受限的情况下,能取得较高的排歧准确率。     

The relationship of the bayes risk to certain separability measures in normal classification

For the problem of classifying an element (e.g., an unknown pattern) into one of two given categories where the associated observables are distributed according to one of two known multivariate normal populations having a common covariance matrix, it is shown that the minimum Bayes risk is a strict monotonic function of certain separability or statistical distance measures regardless of the a priori probabilities and the assigned loss function. However, for the associated conditional expected losses, strict monotonicity holds, if and only if a certain condition dependent on these probabilities and the given loss function is satisfied. These results remain valid for classification problems in which the observable can be transformed by a one-to-one differentiable mapping to normality.