22,911 research outputs found
Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition
The SP theory of computing and cognition, described in previous publications,
is an attractive model for intelligent databases because it provides a simple
but versatile format for different kinds of knowledge, it has capabilities in
artificial intelligence, and it can also function like established database
models when that is required.
This paper describes how the SP model can emulate other models used in
database applications and compares the SP model with those other models. The
artificial intelligence capabilities of the SP model are reviewed and its
relationship with other artificial intelligence systems is described. Also
considered are ways in which current prototypes may be translated into an
'industrial strength' working system
Normalized Web Distance and Word Similarity
There is a great deal of work in cognitive psychology, linguistics, and
computer science, about using word (or phrase) frequencies in context in text
corpora to develop measures for word similarity or word association, going back
to at least the 1960s. The goal of this chapter is to introduce the
normalizedis a general way to tap the amorphous low-grade knowledge available
for free on the Internet, typed in by local users aiming at personal
gratification of diverse objectives, and yet globally achieving what is
effectively the largest semantic electronic database in the world. Moreover,
this database is available for all by using any search engine that can return
aggregate page-count estimates for a large range of search-queries. In the
paper introducing the NWD it was called `normalized Google distance (NGD),' but
since Google doesn't allow computer searches anymore, we opt for the more
neutral and descriptive NWD. web distance (NWD) method to determine similarity
between words and phrases. ItComment: Latex, 20 pages, 7 figures, to appear in: Handbook of Natural
Language Processing, Second Edition, Nitin Indurkhya and Fred J. Damerau
Eds., CRC Press, Taylor and Francis Group, Boca Raton, FL, 2010, ISBN
978-142008592
Information Distance: New Developments
In pattern recognition, learning, and data mining one obtains information
from information-carrying objects. This involves an objective definition of the
information in a single object, the information to go from one object to
another object in a pair of objects, the information to go from one object to
any other object in a multiple of objects, and the shared information between
objects. This is called "information distance." We survey a selection of new
developments in information distance.Comment: 4 pages, Latex; Series of Publications C, Report C-2011-45,
Department of Computer Science, University of Helsinki, pp. 71-7
Normalized Information Distance
The normalized information distance is a universal distance measure for
objects of all kinds. It is based on Kolmogorov complexity and thus
uncomputable, but there are ways to utilize it. First, compression algorithms
can be used to approximate the Kolmogorov complexity if the objects have a
string representation. Second, for names and abstract concepts, page count
statistics from the World Wide Web can be used. These practical realizations of
the normalized information distance can then be applied to machine learning
tasks, expecially clustering, to perform feature-free and parameter-free data
mining. This chapter discusses the theoretical foundations of the normalized
information distance and both practical realizations. It presents numerous
examples of successful real-world applications based on these distance
measures, ranging from bioinformatics to music clustering to machine
translation.Comment: 33 pages, 12 figures, pdf, in: Normalized information distance, in:
Information Theory and Statistical Learning, Eds. M. Dehmer, F.
Emmert-Streib, Springer-Verlag, New-York, To appea
Entropic measures of individual mobility patterns
Understanding human mobility from a microscopic point of view may represent a
fundamental breakthrough for the development of a statistical physics for
cognitive systems and it can shed light on the applicability of macroscopic
statistical laws for social systems. Even if the complexity of individual
behaviors prevents a true microscopic approach, the introduction of mesoscopic
models allows the study of the dynamical properties for the non-stationary
states of the considered system. We propose to compute various entropy measures
of the individual mobility patterns obtained from GPS data that record the
movements of private vehicles in the Florence district, in order to point out
new features of human mobility related to the use of time and space and to
define the dynamical properties of a stochastic model that could generate
similar patterns. Moreover, we can relate the predictability properties of
human mobility to the distribution of time passed between two successive trips.
Our analysis suggests the existence of a hierarchical structure in the mobility
patterns which divides the performed activities into three different
categories, according to the time cost, with different information contents. We
show that a Markov process defined by using the individual mobility network is
not able to reproduce this hierarchy, which seems the consequence of different
strategies in the activity choice. Our results could contribute to the
development of governance policies for a sustainable mobility in modern cities
Qualitative Supervision of Naval Diesel Engine Turbocharger Systems
FAC Intelligent Components and Instruments for Control Applications, Malaga, Spain, 1992This paper presents a qualitative model the diesel engine turbocharger system of a ship. The paper also shows how qualitative models can be use for an intelligent monitoring of the process concerned
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