Classification Using Class Cover Catch Digraphs

class cover catch digraphs based on proximity between training observations. Performance comparisons are presented on synthetic and real examples versus k-nearest neighbors, Fisher's linear discriminant and support vector machines. We demonstrate that the proposed semiparametric classifier has performance approaching that of the optimal parametric classifier in cases for which the optimal is available for comparison.     

西蒙栋的技术物体评析

技术物体是技术样品的进化谱系.由于技术样品存在结构和功能两个交错的进化谱系,西蒙栋转而借助生物有机体的进化谱系进行类比.在讨论技术物体进化动力时,西蒙栋隐喻性地提出技术物体是由纯粹的客观结构元素构成的.修正后的技术物体包含两个元素,技术的内在客观性结构元素和从外界移入的主体意向性元素.     

The Real-Valued Model of Hierarchical Classes

We propose a non-negative real-valued model of hierarchical classes (HICLAS) for two-way two-mode data. Like the other members of the HICLAS family, the non-negative real-valued model (NNRV-HICLAS) implies simultaneous hierarchically organized classifications of all modes involved in the data. A distinctive feature of the novel model is that it yields continuous, non-negative real-valued reconstructed data, which considerably expands the application range of the HICLAS family. The expansion implies a major algorithmic challenge as it involves a move from the typical discrete optimization problems in HICLAS to a mixed discrete-continuous one. To solve this mixed discrete-continuous optimization problem, a two-stage algorithm combining a simulated annealing and an alternating local descent stage is proposed. Subsequently it is evaluated in a simulation study. Finally, the NNRVHICLAS model is applied to an empirical data set on anger.     

量子对象的模糊同一性问题

传统哲学讨论的物质对象几乎都是我们能经验到的宏观对象,宏观对象都是由经典物理学理论所支配。然而,量子理论所讨论的范围是我们平时经验不到的微观领域,这使得传统的哲学因为没有考虑微观的量子对象而有所遗漏,模糊性问题正是如此。量子理论不仅对经典物理理论造成了很大的冲击,并且对传统哲学也造成了一定的冲击。     

Classification of Asymmetric Proximity Data

When clustering asymmetric proximity data, only the average amounts are often considered by assuming that the asymmetry is due to noise. But when the asymmetry is structural, as typically may happen for exchange flows, migration data or confusion data, this may strongly affect the search for the groups because the directions of the exchanges are ignored and not integrated in the clustering process. The clustering model proposed here relies on the decomposition of the asymmetric dissimilarity matrix into symmetric and skew-symmetric effects both decomposed in within and between cluster effects. The classification structures used here are generally based on two different partitions of the objects fitted to the symmetric and the skew-symmetric part of the data, respectively; the restricted case is also presented where the partition fits jointly both of them allowing for clusters of objects similar with respect to the average amounts and directions of the data. Parsimonious models are presented which allow for effective and simple graphical representations of the results.     

墨家辩学的研究对象与逻辑类型

《墨辩》六篇中的“辩”指的是论辩，墨家辩学理论是中国古代关于论辩形式、方法及其规律的科学。墨家辩学中包含有形式逻辑的思想，这种形式逻辑思想届于非形式化的，它通过辩学对自然语言中广义逻辑问题的研究得以表现，因此说，墨家辩学的逻辑类型是中国古代的非形式化的自然语言逻辑。     

On the Construction of Mental Objects in Third and in First Persons

This paper deals with some formal properties of objects that are supposed to be internal to persons, that is, mental structures and mental functions. Depending on the ways of talking about these internal objects, they will appear different. Two types of discourse will be presented, to be called the realist and the nominalist discourses, and for eachdiscourse I will focus upon the construction of `self'.The realist discourse assumes an identity between the person and his construction of himself. I will illustrate this discourse in terms of Descartes' ideas on himself as a `thinking substance'. The nominalist discourse assumes an impossibility to attain this identity, and instead to imply a complementarity between the person and his self-construction. I will illustrate this discourse in terms of the problems both William James and Sartre discerned when a conscious person chases after his own consciousness (termed `judging thought' and `pour-soi' respectively).     

Annual Conference of the Classification Society

Call for Abstracts

Annual Conference of the Classification Society     

医用内窥镜的概念与分类--以各专利分类体系为视角

在拥有海量专利文献的数据库中有效地利用分类号可以更便捷地获取所需信息,而各个分类号不同的细分特点也在一定程度上反映了世界主要经济地区在该领域的技术发展趋势。文章以当今各专利分类体系为视角,探讨医用内窥镜的概念及分类。     

关于当代生命科学研究对象的划界、分析与表征的讨论

本文讨论了当代生命科学研究对象的划界、分析与表征的问题,首先围绕三个分主题对国际生物学的历史、哲学和社会学研究协会(ISHPSSB)2011年双年会的相关报告进行介绍并评论:关于基因组学与后基因组学的本体论和认识论问题;关于自然选择理论的扩展性的研究;对于生物学个体性概念的讨论,然后介绍了作者在共生语境下对生物学个体概念的研究进展。文章指出,对同一对象的不同表征模式以及对同一概念的不同分析方式,体现了两种科学传统在当代生命科学中的冲突与交汇;系统生物学力图综合不同研究范式,整合不同生命组织的层次,对其独特的研究方法、概念体系进行分析和讨论,将是未来生物学哲学的热门研究课题。     

One-Mode Classification of a Three-Way Data Matrix

X is the automatic hierarchical classification of one mode (units or variables or occasions) of X on the basis of the other two. In this paper the case of OMC of units according to variables and occasions is discussed. OMC is the synthesis of a set of hierarchical classifications Delta obtained from X; e.g., the OMC of units is the consensus (synthesis) among the set of dendograms individually defined by clustering units on the basis of variables, separately for each given occasion of X. However, because Delta is often formed by a large number of classifications, it may be unrealistic that a single synthesis is representative of the entire set. In this case, subsets of similar (homegeneous) dendograms may be found in Delta so that a consensus representative of each subset may be identified. This paper proposes, PARtition and Least Squares Consensus cLassifications Analysis (PARLSCLA) of a set of r hierarchical classifications Delta. PARLSCLA identifies the best least-squares partition of Delta into m (1 <= m <= r) subsets of homogeneous dendograms and simultaneously detects the closest consensus classification (a median classification called Least Squares Consensus Dendogram (LSCD) for each subset. PARLSCLA is a generalization of the problem to find a least-squares consensus dendogram for Delta. PARLSCLA is formalized as a mixed-integer programming problem and solved with an iterative, two-step algorithm. The method proposed is applied to an empirical data set.     

Structural Classification Analysis of Three-Way Dissimilarity Data

The paper presents a methodology for classifying three-way dissimilarity data, which are reconstructed by a small number of consensus classifications of the objects each defined by a sum of two order constrained distance matrices, so as to identify both a partition and an indexed hierarchy. Specifically, the dissimilarity matrices are partitioned in homogeneous classes and, within each class, a partition and an indexed hierarchy are simultaneously fitted. The model proposed is mathematically formalized as a constrained mixed-integer quadratic problem to be fitted in the least-squares sense and an alternating least-squares algorithm is proposed which is computationally efficient. Two applications of the methodology are also described together with an extensive simulation to investigate the performance of the algorithm.     

可用性及其对技术人工物主客体关系的影响

可用性表征技术人工物在客观上与主体需求相符合程度,它经由三个阶段生成,促进技术人工物主客体的分化与联合。可用性以设计者的意向赋予人工物的潜在功能与使用者在操作中实际能发挥的功能两者间的比较为反馈,对技术人工物主客体互动关系产生影响。     

A New Representation of Interval Symbolic Data and Its Application in Dynamic Clustering

In this study, we consider the type of interval data summarizing the original samples (individuals) with classical point data. This type of interval data are termed interval symbolic data in a new research domain called, symbolic data analysis. Most of the existing research, such as the (centre, radius) and [lower boundary, upper boundary] representations, represent an interval using only the boundaries of the interval. However, these representations hold true only under the assumption that the individuals contained in the interval follow a uniform distribution. In practice, such representations may result in not only inconsistency with the facts, since the individuals are usually not uniformly distributed in many application aspects, but also information loss for not considering the point data within the intervals during the calculation. In this study, we propose a new representation of the interval symbolic data considering the point data contained in the intervals. Then we apply the city-block distance metric to the new representation and propose a dynamic clustering approach for interval symbolic data. A simulation experiment is conducted to evaluate the performance of our method. The results show that, when the individuals contained in the interval do not follow a uniform distribution, the proposed method significantly outperforms the Hausdorff and city-block distance based on traditional representation in the context of dynamic clustering. Finally, we give an application example on the automobile data set.