3,394 research outputs found
IMMANUEL WALLERSTEIN'S WORLD SYSTEM THEORY
World-systems analysis is not a theory, but an approach to social analysis and social change developed, among others by the Immanuel Wallerstein. Professor Wallerstein writes in three domains of world-systems analysis: the historical development of the modern world-system; the contemporary crisis of the capitalist world-economy; the structures of knowledge. The American anlyst rejects the notion of a "Third World", claiming there is only one world connected by a complex network of economic exchange relationship. Our world system is characterized by mechanisms which bring about a redistribution of resources from the periphery to the core. His analytical approach has made a significant impact and established an institutional base devoted to the general approach.World system, core, semi-periphery, periphery, external regions
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A comparison of fuzzy approaches to e-commerce review rating prediction
This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor
What Is a Macrostate? Subjective Observations and Objective Dynamics
We consider the question of whether thermodynamic macrostates are objective
consequences of dynamics, or subjective reflections of our ignorance of a
physical system. We argue that they are both; more specifically, that the set
of macrostates forms the unique maximal partition of phase space which 1) is
consistent with our observations (a subjective fact about our ability to
observe the system) and 2) obeys a Markov process (an objective fact about the
system's dynamics). We review the ideas of computational mechanics, an
information-theoretic method for finding optimal causal models of stochastic
processes, and argue that macrostates coincide with the ``causal states'' of
computational mechanics. Defining a set of macrostates thus consists of an
inductive process where we start with a given set of observables, and then
refine our partition of phase space until we reach a set of states which
predict their own future, i.e. which are Markovian. Macrostates arrived at in
this way are provably optimal statistical predictors of the future values of
our observables.Comment: 15 pages, no figure
Predictive PAC Learning and Process Decompositions
We informally call a stochastic process learnable if it admits a
generalization error approaching zero in probability for any concept class with
finite VC-dimension (IID processes are the simplest example). A mixture of
learnable processes need not be learnable itself, and certainly its
generalization error need not decay at the same rate. In this paper, we argue
that it is natural in predictive PAC to condition not on the past observations
but on the mixture component of the sample path. This definition not only
matches what a realistic learner might demand, but also allows us to sidestep
several otherwise grave problems in learning from dependent data. In
particular, we give a novel PAC generalization bound for mixtures of learnable
processes with a generalization error that is not worse than that of each
mixture component. We also provide a characterization of mixtures of absolutely
regular (-mixing) processes, of independent probability-theoretic
interest.Comment: 9 pages, accepted in NIPS 201
Consistency of Maximum Likelihood for Continuous-Space Network Models
Network analysis needs tools to infer distributions over graphs of arbitrary
size from a single graph. Assuming the distribution is generated by a
continuous latent space model which obeys certain natural symmetry and
smoothness properties, we establish three levels of consistency for
non-parametric maximum likelihood inference as the number of nodes grows: (i)
the estimated locations of all nodes converge in probability on their true
locations; (ii) the distribution over locations in the latent space converges
on the true distribution; and (iii) the distribution over graphs of arbitrary
size converges.Comment: 21 page
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