We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous ...

In this paper, we define a Mathematical model of program structure. Mathematical model of program structure defined here provides unified mathematical treatment of program structure, which reveals that a program is a large and finite set of embedded binary relations between current statement and previous ones. Then, a program is considered as a composed listing and a logical combination of mult...

Since electroencephalographic (EEG) signal may be considered chaotic, Nonlinear Dynamics and Deterministic Chaos Theory may supply effective quantitative descriptors of EEG dynamics and of underlying chaos in the brain. We have used Karhunen-Loeve decomposition of the covariance matrix of the EEG signal to analyse EEG signals of 4 healthy subjects, under drug-free condition and under the influe...

Measuring the complexity of software has been an insoluble problem in software engineering. Complexity measures can be used to predict critical information about testability, reliability, and maintainability of software systems from automatic analysis of the source code. During the past few years, many complexity measures have been invented based on the emerging Cognitive Informatics discipline...

We apply the statistical measure of complexity, introduced by López-Ruiz, Mancini, and Calbet (LMC), to uniform Fermi systems. We investigate the connection between information and complexity measures with the strongly correlated behavior of various Fermi systems as nuclear matter, electron gas, and liquid helium. We examine the possibility that LMC complexity can serve as an index quantifying ...

The structure of the human brain changes in a variety of ways as we age. While a sizeable literature has examined age-related differences in cortical thickness, and to a lesser degree, gyrification, here we examined differences in cortical complexity, as indexed by fractal dimensionality in a sample of over 400 individuals across the adult lifespan. While prior studies have shown differences in...

This paper introduces a formalism which combines reliability and complexity in a measure of performance for Intelligent Machines. For a given desired accuracy, diierent algorithms may be available which are reliable enough. Reliability is deened as the probability that an algorithm meets, at run-time, the accuracy speciications which guided its design. However it is important to have a means of...

In previous work we have considered the usercentered complexity of a hierarchy of abstractions as observed from a user’s perspective. We hypothesized that, given a perspective, there should be a variety of complexities depending on the granularity of the abstraction and the measure of complexity chosen. In this paper we consider an optimization based on the Minimum Description Length complexity...

The Web 2.0 and the inexpensive cost of storage have pushed towards an exponential growth in the volume of collected and produced data. However, the integration of distributed and heterogeneous data sources has become the bottleneck for many applications, and it therefore still largely relies on manual tasks. One of this task, named matching or alignment, is the discovery of correspondences, i....

Research on how information should be presented during inductive category learning has identified both interleaving of categories and blocking by category as beneficial for learning. Previous work suggests that this mixed evidence can be reconciled by taking into account withinand betweencategory similarity relations. In this paper we present a new moderating factor. One group of participants s...