A Generalization of Cobham's Theorem

If a nonperiodic sequence X is the image by a morphism of a fixed point of both a primitive substitution σ and a primitive substitution τ , then the dominant eigenvalues of the matrices of σ and τ are multiplicatively dependent. This is the way we propose to generalize Cobham's theorem. Received April 1996, and in final form April 1997.     

一种分明矩阵法的推广

属性约简是知识发现的重要研究内容,也是Rough集理论的核心内容之一。目前,最常用的计算所有属性约简方法是Skowron的分明矩阵法。文中在深入研究Skowron分明矩阵法的基础上,对Skowron的分明矩阵法进行了推广,提出了广义分明矩阵的属性约简方法,并给出了运行实例。     

基于限定的规划识别问题求解

该文把McCarthy的限定理论同规划识别结合起来,在限定中研究规划识别问题,证明了在一定的限制下,由观察到的现象求出的最小规划集与对这些现象作限定获得的解集是一样的,以此为基础,文中提出了一种用限定求解规划识别问题的方式,这种方法把Kautz提出的规划识别表示形式做了某些改变,求解的过程中把二阶限定的表示形式转化为一阶形式,这种一阶形式的限定结果可以用逐点限定的方法直接求得,因为利用了逐点限定的这一特点,该文的方法对限定的计算过程中可以用机器自动完成。     

A Generalization of Local Fuzzy Structures

The class of bounded residuated lattice ordered monoids Rl-monoids) contains as proper subclasses the class of pseudo BL-algebras (and consequently those of pseudo MV-algebras, BL-algebras and MV-algebras) and of Heyting algebras. In the paper we introduce and investigate local bounded Rl-monoids which generalize local algebras from the above mentioned classes of fuzzy structures. Moreover, we study and characterize perfect bounded Rl-monoids.     

关键方程的新推广

给出了关键方程的全新的推广,构造了一个齐次关键模方程,并用域F上的两个变元的多项式环F[x,y]的齐次理想刻画该方程的解空间;证明了齐次关键模方程可以用来解决卷积码的盲识别问题,这是一个全新的研究课题,在智能通信、信息截获和密码分析等领域有重要的应用;利用该方法得到的二元多项式齐次理想Gr（o）bner基的快速算法,给出了求解齐次关键模方程的快速算法,也给出了详细的计算实例.大量的实验也证实了该文的各项理论分析结果.     

A Generalization of Jumps and Labels

This paper describes a new language feature that is a hybrid of labels and procedures. It is closely related to jumping out of a functional subroutine, and includes conventional labels and jumping as a special, but probably not most useful, case. It is independent of assignment, ie., it can be added to a purely-functional (non-imperative) system (such as LISP without pseudo-functions or program feature). Experiments in purely functional programming suggest that its main use will be in success/failure situations, and failure actions. This innovation is incorporated in the projected experimental system, ISWIM.     

Explanation-Based Generalization: A Unifying View

The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to for-mulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.     

A Generalization of the Convex Kakeya Problem

Given a set of line segments in the plane, not necessarily finite, what is a convex region of smallest area that contains a translate of each input segment? This question can be seen as a generalization of Kakeya’s problem of finding a convex region of smallest area such that a needle can be rotated through 360 degrees within this region. We show that there is always an optimal region that is a triangle, and we give an optimal Θ(nlogn)-time algorithm to compute such a triangle for a given set of n segments. We also show that, if the goal is to minimize the perimeter of the region instead of its area, then placing the segments with their midpoint at the origin and taking their convex hull results in an optimal solution. Finally, we show that for any compact convex figure G, the smallest enclosing disk of G is a smallest-perimeter region containing a translate of every rotated copy of G.     

A Generalization of a Random Walk Problem

Let X _1,X 2,… be i.i.d. random variables with P(X ₁=-k) ∈ (0,1) for some k ∈ N, S ₁=X ₁ +···+ X ₁. We derive an exact expression for the probability that a particle performing a simple random walk will never cross a given straight line, i.e., P(S_l > lα + β for some l ∈ N), where α ∈ [?k,k], β S 0 are rational. Further the exact distribution of sup {S_l /l|≥1} is obtained.     

一种新型制图综合软件的设计模式

本文介绍了一种新型制图综合软件的设计模式。在软件设计上使用工厂类的设计模式思想，程序结构上降低了地图数据处理与制图综合的具体算法之间的耦合度；又使用函数类技术隐蔽制图综合过程的实现细节。使得该系统对于综合过程具有较高层次的抽象，具有高度的易扩充性和高度的安全性。     

A Generalization of DPCM for Digital Image Compression

A natural generalization of two-dimensional digital pulsecode modulations (DPCM) has been used to handle the compression of images for human eyes only. It shows that by ignoring the mean-square error (MSE) and wisely arranging the errors in the right place, a simple method can achieve very good results.     

Cascade Generalization

Using multiple classifiers for increasing learning accuracy is an active research area. In this paper we present two related methods for merging classifiers. The first method, Cascade Generalization, couples classifiers loosely. It belongs to the family of stacking algorithms. The basic idea of Cascade Generalization is to use sequentially the set of classifiers, at each step performing an extension of the original data by the insertion of new attributes. The new attributes are derived from the probability class distribution given by a base classifier. This constructive step extends the representational language for the high level classifiers, relaxing their bias. The second method exploits tight coupling of classifiers, by applying Cascade Generalization locally. At each iteration of a divide and conquer algorithm, a reconstruction of the instance space occurs by the addition of new attributes. Each new attribute represents the probability that an example belongs to a class given by a base classifier. We have implemented three Local Generalization Algorithms. The first merges a linear discriminant with a decision tree, the second merges a naive Bayes with a decision tree, and the third merges a linear discriminant and a naive Bayes with a decision tree. All the algorithms show an increase of performance, when compared with the corresponding single models. Cascade also outperforms other methods for combining classifiers, like Stacked Generalization, and competes well against Boosting at statistically significant confidence levels.     

一种替换类隐写术算法的统一模型

隐写术是信息隐藏一个重要的研究分支。论文给出一种新的基于DCT域的可以实现“大容量”、“盲提取”的隐写术算法,经过少许的变动,算法可以成为一种能够涵盖空域、变换域大部分替换类算法的“统一模型”。算法的描述基于DCT域,但其思想可适用于其它变换域。实验结果表明:在满足“不可见”、“盲提取”的前提下,算法能正确嵌入/提取“大容量”的秘密信息。     

基于粗糙集的数据概化算法

应用粗糙集理论对大型数据库中的数据概化进行了研究,提出了对数据的动态概念层次划分算法,用以对相关数据集进行属性约简,并在此基础上挖掘概化特征,动态维护概念层次树。