核函数对v-支持向量机的泛化能力影响分析

支持向量机在模式分类中得到了很好的应用。对于线性不可分的样本空间，需要寻找核函数，将线性不可分的样本集映射到另一个高维线性空间。支持向量机的泛化能力直接影响其实际应用效果。本文讨论了v-支持向量机的泛化能力的一个估计，定性分析了核函数对v-支持向量机的泛化能力的影响，并就高斯核函数对v-支持向量机的泛化能力的
的影响进行了具体分析。     

AdaBoost算法研究进展与展望

AdaBoost是最优秀的Boosting算法之一, 有着坚实的理论基础, 在实践中得到了很好的推广和应用. 算法能够将比随机猜测略好的弱分类器提升为分类精度高的强分类器, 为学习算法的设计提供了新的思想和新的方法. 本文首先介绍Boosting猜想提出以及被证实的过程, 在此基础上, 引出AdaBoost算法的起源与最初设计思想;接着, 介绍AdaBoost算法训练误差与泛化误差分析方法, 解释了算法能够提高学习精度的原因;然后, 分析了AdaBoost算法的不同理论分析模型, 以及从这些模型衍生出的变种算法;之后, 介绍AdaBoost算法从二分类到多分类的推广. 同时, 介绍了AdaBoost及其变种算法在实际问题中的应用情况. 本文围绕AdaBoost及其变种算法来介绍在集成学习中有着重要地位的Boosting理论, 探讨Boosting理论研究的发展过程以及未来的研究方向, 为相关研究人员提供一些有用的线索. 最后,对今后研究进行了展望, 对于推导更紧致的泛化误差界、多分类问题中的弱分类器条件、更适合多分类问题的损失函数、 更精确的迭代停止条件、提高算法抗噪声能力以及从子分类器的多样性角度优化AdaBoost算法等问题值得进一步深入与完善.     

ON THE CONNECTION BETWEEN THE COMPLEXITY AND CREDIBILITY OF INFERRED MODELS

Abstract

The connection between the simplicity of scientific theories and the credence attributed to their predictions seems to permeate the practice of scientific discovery. When a scientist succeeds in explaining a set of nobservations using a model Mof complexity c then it is generally believed that the likelihood of finding another explanatory model with similar complexity but leading to opposite predictions decreases with increasing nand decreasing c. This paper derives formal relationships between n, c and the probability of ambiguous predictions by examining three modeling languages under binary classification tasks: perceptrons, Boolean formulae, and Boolean networks. Bounds are also derived for the probability of error associated with the policy of accepting only models of complexity not exceeding c. Human tendency to regard the simpler as the more trustworthy is given a qualified justification.     

模糊最小包含球支持向量机

为提高支持向量机的模式分类性能,综合模糊支持向量机和球形支持向量机等方法,提出一种模糊最小包含球(FMEB)支持向量机,对于模式分类问题,通过引入模糊隶属度,寻找2个分别包含二类模式的同心最小包含球,使类间间隔最大化,同时二类模式类内分布最小化,从而增强泛化性和鲁棒性。实验结果证明FMEB的模式分类性能优于其他方法。     

采用案例归纳推理进行道路网智能选取

自动制图综合集技术、艺术与制图人员经验于一体,长期以来其自动化、智能化研究进展缓慢。基于机器学习的智能化自动综合也成为了制图综合发展过程中必须解决而仍未得到很好解决的核心难题之一。本文提出基于案例归纳学习的道路网智能选取方法。该方法以制图专家道路网选取案例库为学习对象,以决策树算法为推理机,从专家案例库中自动归纳、推理来获取决策树,并转化为满足计算机自动执行的规则集,据此来进行道路网自动选取。从而解决了把难以形式化表达的制图专家经验自动转化为满足计算机自动综合要求的规则,并据此进行智能化自动综合这一难题。最后,采用实例对本方法进行了验证,实验结果表明,该方法能够从专家案例库中自动获取核心规则,并进行自动综合,综合结果能够有效地反映制图专家的制图综合经验,同时具有普适性,从而为智能化自动制图综合发展探索了新的途径。     

增量式的多变量决策树构造算法研究

针对增量数据集,结合粗糙集理论和多变量决策树的优点,给出了增量式的多变量决策树构造算法。该算法针对新增样本与已有规则集产生矛盾,即条件属性相匹配,而决策属性不匹配的情况,计算条件属性相对于决策属性的核,如果核不为空,则计算核相对于决策属性的相对泛化,根据不同的结果形成不同的子集,最终形成不同的决策树分支。该算法很好地避免了在处理增量数据集时,不断重构决策树。实例证明该算法的正确性,对处理小增量数据集具有良好的性能。     

面向GIS的城市防洪图制作

介绍了以Mapinfo,Coreldraw为基本制图，制版平台，设计与制作城防工程电子地图的过程，探讨了面向GIS的城防图概括制作技术，并应用于城市防洪图集的制作中，取得了较好的效果。     

Commentary on Meehl and Waller's (2002) Path Analysis and Verisimilitude.

P. E. Meehl and N. G. Waller's (see record 2002-18342-001) proposed method may not yield unique solutions for model parameters nor unique solutions for model lack of fit. The author argues from a naturalistic-cognitive philosophy of science that science seeks objective knowledge and that hypothesis testing is central to achieving that goal. It is also argued that P. E. Meehl and N. G. Waller's proposal blurs the distinction between hypothesis testing and explorations of the data seeking an optimal model to serve as a prospective inductive generalization. But it is noted that inductive generalizations are never unique and must be tested to eliminate those that reflect subjective aspects of the researcher's methods and points of view. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization.

J. D. Smith and colleagues (J. P. Minda & J. D. Smith, 2001; J. D. Smith & J. P. Minda, 1998, 2000; J. D. Smith, M. J. Murray, & J. P. Minda, 1997) presented evidence that they claimed challenged the predictions of exemplar models and that supported prototype models. In the authors' view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al's experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

浅论雷达装备"双三化""三新"与"三性"

雷达装备的"三性"是装备战斗力最重要的组成部分,是使用方关注的焦点."双三化"和"三新"是"三性"的基础和动力.文中从雷达装备的"双三化"、"三新"和"三性"概念入手,深入分析了"双三化"与"三新"的相互关系,对雷达装备技术跨代的推动作用,与雷达装备"三性"的内在联系及影响,三者目前的发展动态及未来发展方向.