Showing papers in "Information Sciences in 1975"

TL;DR: Much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior.

TL;DR: Empirical comparisons show the effectiveness of the new members of the family of parsing algorithms, which appear to be definitely better than Earley's algorithms, except for a few pathological grammars.

TL;DR: Simple characterizations of these classes of fuzzy languages realized by probabilistic, max-product and maximin automata are given, which reduce to Nerode's Theorem in the deterministic case.

TL;DR: It is shown that some context-sensitive languages can be generated by type 3 ∗-L- fuzzy grammars with cut points, and that for type 2 L -fuzzy Grammars, Chomsky and Greibach normal form can be constructed as an extension of corresponding notion in the theory of formal grammarmars.

TL;DR: Two equivalent formulations of max-product grammars are presented and it is shown that /oL contains the family of regular languages as a proper subfamily and that there are context-free and stochastic languages which are not in /oS.

TL;DR: A methodology for analyzing interdependence in character sequences found that dependence on immediate neighbor is dominant, decreases monotonically with increasing separation, and becomes statistically insignificant in cases of separation greater than about 5 spaces.

TL;DR: A quantum-mechanical stochastic approximation algorithm is presented, which, under certain conditions, is asymptotically efficient, even if the density operators for different parameter values do not commute.

TL;DR: A new algorithm containing the generalized Frisch labeling algorithm is developed to solve the chaining problem and sequentially yields subclusters from a parent cluster when a splitting criterion is satisfied.

TL;DR: The injection operator used by McKean to construct Brownian paths on a Lie group is employed to formulate a class of signal detection problems on matrix Lie groups and a likelihood ratio formula is derived as a function of up-dated observation.

TL;DR: It is demonstrated, in particular, that time-delays are fundamental to Fibonacci string sequences (or locally concatenative sequences).

TL;DR: In this article, the problem of discrete-time demodulation of angle-modulated analog signals transmitted over fading channels, with emphasis on Rayleigh and Rician channels, was addressed.

TL;DR: It is shown that every random environment discussed up to the present is a special case of this machine and the system consisting of this environment and an automaton is equivalent to an autonomous stochastic automaton.

TL;DR: An extended version of Dvoretzky's theorem is derived from a sufficient condition that estimation error converges to zero, both in the mean square and with probability one for noise-free multivariable dynamical systems.

TL;DR: A nonlinear distance metric criterion for feature selection in the measurement space is proposed, which is not only a more reliable measure of class separability than criteria based on the Euclidean distance metric but also computationally more efficient.

TL;DR: A subjective clustering and description of the books is provided which readily identifies the books in any particular area of pattern recognition.

TL;DR: Computer simulations of the Mr,k, model in the context of feature selection in an unsupervised environment has demonstrated the superiority of the model over similar models without this multi-state-choice innovation.

TL;DR: The spectral representation theorem for homogeneous fields is proved, and various second order properties resulting from the application of linear difference and differential operators to such fields are deduced.

TL;DR: It is shown that no concept formation program is superior over the whole range of possible concepts, and if a program is modified so as to improve its performance on some concepts, then itsperformance on other concepts must necessarily deteriorate.

TL;DR: This work investigates the relations between the tree representation of a prefix code C, its patterns, the minimal automaton which recognizes C ∗ , and the homomorphic images of the underlying algebra of .

TL;DR: Two different clustering techniques, one based on covariance assumptions, the other using Euclidean distance, are compared by applying them to data comprised of content category frequencies derived from selected judicial opinions of the Supreme Court, showing a capability for grouping similar opinions.

TL;DR: A simple and direct proof of the general Bucy representation theorem is obtained by the application of thegeneral Bayes rule.

TL;DR: In this article, the authors studied the stability of a system of nonlinear random integral equations of the Volterra type of the form x(t;ω) = h(t, x (t, τ, x(τ, ω))dτ, is presented where t ϵ R+ = {t:t ≥ 0}, ωϵΩ, Ω being the underlying set of a complete probability measure space.

TL;DR: The objective of this paper is to present a general realization theory which will cover not only nonlinear systems but also those systems that are not necessarily continuous or even defined on the topological spaces.

TL;DR: It is demonstrated that the capabilities in principle of this modified type of Turing machine are the same as for Turing machines, but that the speed of recognition is reduced.

TL;DR: A counterexample relevant to both papers is given and a mistake is pointed out in the proof given in [1]: certain control laws were claimed to be optimal.

TL;DR: A queue-theoretic model is developed for estimating response time for a message at an information center and a Markovian model for the total network cost is derived.

TL;DR: Two characterization theorems of /oM (/oL) are given, one is in terms of homomorphism and intersection, and the other is in Terms of AFA with control set.

TL;DR: An investigation has been made of the relation between the optimal classification error for two composite classes and the optimal Classification error for certain pairs of subclasses (one from each composite class).

TL;DR: Using the theory of Stochastic Stability and Control, it is shown that there exists an upper bound of the probability with which stochastic automaton goes back to an unfavorable state within some finite time.

TL;DR: This paper develops smoothing algorithms in the mixed-time framework appropriate to the interpolation problem, and then compares their results with those obtained previously in discrete time.