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STRUCTURE OF DETERMINATIVE SUBSPACE IN TRIANGULAR CELL SPACE : INFORMATION SCIENCE APPROACH TO BIOMATHEMATICS, VIII 三角形细胞空间中确定子空间的结构:生物数学的信息科学方法,8
Pub Date : 1971-03-01 DOI: 10.5109/13056
M. Yamaguchi
The notion of stable configuration in a Sn' cell space which is invariant under any application of local majority transformation (LMT) was introduced in Kitagawa and Yamaguchi [1] and the notion of determinative subspace in a 4(71) cell space which determines a structure of a stable configuration was introduced in our paper [2]. Some structual properties of determinative subspace in ZI(n) cell space were suggested throughout various examples of determinative subspace in Kitagawa and Yamaguchi [3]. According to the definitions of generative and non-generative determinative subspace in 4(n) cell space given by Kitagawa [4] in view of propagation of determined cells, these examples are mostly concerned with those of generative determinative subspace in 4(n) cell space. The purpose of this paper is to give a deeper investigation for the constructions of determinative subspaces in 4(n) cell space. In SECTION 2 we shall introduce several notions which are indispensable for a construction procedure of generative determinative subspace in 4(m) cell space such as a convex set, a spiny convex set and a two-cell extension of a set and so on. In SECTION 3 we shall give a certain type of construction procedure of any generative determinative subspace in zl(n) cell space. In APPENDIX we shall give an example of our construction process of a generative determinative subspace obtained by using this proceduce. In SECTION 4 we shall introduce several notions of elementary subsets and superposition of elementary subsets and decomposition of the whole cell space into a family of superposed elementary subsets. These nations are fundamental tools for proving THEOREM 3 and 4, which give us a construction procedure and hence a structural characteristic feature of any non-generative determinative subspace in 4(n) cell space. The last SECTION 5 is devoted to the another proof of THEOREM 2 in our previours paper [2] which appeals to LEMMA 4 in the present paper prepared for giving our construction procedure of non-generative determinative determinative
在Kitagawa和Yamaguchi[1]中引入了Sn单元空间中稳定位形的概念,它在任何局部多数变换(local majority transformation, LMT)的应用下都是不变的。在我们的论文[2]中引入了4(71)单元空间中确定稳定位形结构的确定子空间的概念。通过Kitagawa和Yamaguchi[3]中的各种确定子空间的例子,提出了ZI(n)单元空间中确定子空间的一些结构性质。根据Kitagawa[4]针对确定细胞的繁殖给出的4(n)细胞空间中生成和非生成的确定子空间的定义,这些例子主要涉及4(n)细胞空间中生成确定子空间的定义。本文的目的是对4(n)元空间中确定子空间的构造进行更深入的研究。在第二节中,我们将介绍在4(m)元空间中生成确定子空间的构造过程中不可缺少的几个概念,如凸集、刺凸集和集合的两元扩展等。在第3节中,我们将给出zl(n)单元空间中任意生成确定子空间的一类构造过程。在附录中,我们将给出一个用这个方法得到的生成式确定子空间的构造过程的例子。在第4节中,我们将介绍基本子集和基本子集的叠加以及将整个细胞空间分解为叠加的基本子集族的几个概念。这些国家是证明定理3和定理4的基本工具,定理3和定理4给了我们一个构造过程,从而给出了4(n)单元空间中任何非生成确定子空间的结构特征特征。最后的第5节是我们以前的论文[2]中定理2的另一个证明,它引用了本文中为给出非生成行列式的构造过程而准备的引理4
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引用次数: 2
STABLE CONFIGURATION UNDER LOCAL MAJORITY TRANSFORMATIONS ON CELL SPACE : INFORMATION SCIENCE APPROACH TO BIOMATHEMATICS, V 细胞空间局部多数变换下的稳定构型:生物数学的信息科学方法,[j]
Pub Date : 1971-03-01 DOI: 10.5109/13055
M. Yamaguchi
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引用次数: 0
SEQUENTIAL ESTIMATION OF A CONTINUOUS PROBABILITY DENSITY FUNCTION AND MODE 连续概率密度函数和模式的序贯估计
Pub Date : 1971-03-01 DOI: 10.5109/13049
Hajime Yamato
The purpose of this paper is to discuss statistical properties of an estimate of a probability density function based on the first n observations under the assumption of continuity or uniform continuity of the probability density function in case where we observe a sequence of random vectors which come from a population with the probability density function. Let X1, X2,X3,••• be a sequence of independent identically distributed m-dimensional random vectors having a probability density function f(x). Suppose the first n observations be denoted by X1, X2, ••• Xn. Then a natural estimate of the probability density function f(x) may be denoted as follows for a suitable positive constant h
本文的目的是讨论在概率密度函数连续或均匀连续的假设下,基于前n个观测值的概率密度函数估计的统计性质,当我们观察到的随机向量序列来自具有概率密度函数的总体。设X1, X2,X3,•••是具有概率密度函数f(x)的独立同分布的m维随机向量序列。假设前n个观测值用X1, X2,••••Xn表示,那么对于合适的正常数h,概率密度函数f(x)的自然估计可以表示为
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引用次数: 117
APPLICATION OF KITAGAWA'S FUNCTIONAL INTEGRAL TO SOLUTIONS OF NON-LINEAR INTEGRAL EQUATIONS OF TWO VARIABLES 北川泛函积分在二元非线性积分方程解中的应用
Pub Date : 1971-03-01 DOI: 10.5109/13052
P. Strait
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引用次数: 0
ON THE PATTERN CLASSIFICATION PROBLEMS BY LEARNING I 基于学习的模式分类问题
Pub Date : 1970-03-01 DOI: 10.5109/13043
Kensuke Tanaka
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引用次数: 3
FUNDAMENTAL THEOREMS IN A BAYES CONTROLLED PROCESS 贝叶斯控制过程中的基本定理
Pub Date : 1970-03-01 DOI: 10.5109/13048
N. Furukawa
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引用次数: 0
A NOTE ON THE EFFICIENCY OF TAMURA'S $ Q $ 关于田村$ q $的效率的注释
Pub Date : 1970-03-01 DOI: 10.5109/13042
T. Yanagawa
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引用次数: 3
STOPPED DECISION PROCESS ON COMPACT METRIC SPACES 紧度量空间上的停止决策过程
Pub Date : 1970-03-01 DOI: 10.5109/13044
Seiichi Iwamoto, 岩本 誠一
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引用次数: 0
A FURTHER COMPARISON OF TWO-SAMPLE NONPARAMETRIC TESTS FOR DISPERISON I 离散度I的两样本非参数检验的进一步比较
Pub Date : 1970-03-01 DOI: 10.5109/13041
T. Yanagawa
various attempts have been made to construct nonparametric tests by authors such as Mood [9], Sukhatme [11], Freund and Ansari [7], Barton and David [4], Tamura [12], Siegel and Tukey [10], Capon [5], Klotz [8] and others. But as Klotz [8] has pointed out, there is (at least asymptotically) an equivalence in the sense of test statistics among the test of Freund-Ansari, Barton-David and Siegel-Turkey, and hence we shall restrict our consideration to the tests of Sukhatme, Tamura, Mood and Freund-Ansari, whose test statistics are given as follows. Let denote min (x, y) and max (x, y) by x Ay and x V y, respedtively. Sukhatme's T: 1 ni T1" = E E 46(xi, YJ)
Mood[9]、Sukhatme[11]、Freund and Ansari[7]、Barton and David[4]、Tamura[12]、Siegel and Tukey[10]、Capon[5]、Klotz[8]等人尝试构建非参数检验。但正如Klotz[8]所指出的那样,Freund-Ansari、Barton-David和Siegel-Turkey的检验在检验统计量意义上存在(至少是渐近的)等价,因此我们将把我们的考虑限制在Sukhatme、Tamura、Mood和Freund-Ansari的检验上,他们的检验统计量给出如下。令最小(x, y)和最大(x, y)分别用x Ay和x vy表示。Sukhatme's T: 1 ni T1 ' = E E 46(xi, YJ)
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引用次数: 2
SOME APPLICATIONS OF STOCHASTIC APPROXIMAION METHOD 随机逼近方法的一些应用
Pub Date : 1970-03-01 DOI: 10.5109/13046
H. Fukamichi
Stochastic approximation method first introduced by Robbins-Monro [10] has been proved to be very useful for a learning system in the sense that its algorithm is very simple and that, at any given time in the learning process, the past samples are not required to retain in memory as shown by Albert and Gardner [1] , Blum [3] etc. In this paper we are concerned with its applications . After giving some preliminaries and notations in Section 2, we shall treat in Section 3 the problem of finding threshold elements, which is fundamental in pattern classification . In Section 4 we shall consider the problem of parameter identification in linear system with an additive noise.
由Robbins-Monro[10]首次引入的随机逼近方法已经被证明对学习系统非常有用,因为它的算法非常简单,并且在学习过程中的任何给定时间,过去的样本都不需要保留在记忆中,如Albert和Gardner [1], Blum[3]等。本文主要讨论它的应用。在第2节给出一些初步的说明和记号之后,我们将在第3节处理寻找阈值元素的问题,这是模式分类的基础。在第4节中,我们将考虑具有加性噪声的线性系统的参数辨识问题。
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引用次数: 0
期刊
Bulletin of Mathematical Statistics
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