14,572 research outputs found
Multi-learner based recursive supervised training
In this paper, we propose the Multi-Learner Based Recursive Supervised Training (MLRT) algorithm which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3 are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system
Identikit 2: An Algorithm for Reconstructing Galactic Collisions
Using a combination of self-consistent and test-particle techniques,
Identikit 1 provided a way to vary the initial geometry of a galactic collision
and instantly visualize the outcome. Identikit 2 uses the same techniques to
define a mapping from the current morphology and kinematics of a tidal
encounter back to the initial conditions. By requiring that various regions
along a tidal feature all originate from a single disc with a unique
orientation, this mapping can be used to derive the initial collision geometry.
In addition, Identikit 2 offers a robust way to measure how well a particular
model reproduces the morphology and kinematics of a pair of interacting
galaxies. A set of eight self-consistent simulations is used to demonstrate the
algorithm's ability to search a ten-dimensional parameter space and find
near-optimal matches; all eight systems are successfully reconstructed.Comment: 14 pages, 8 figures. Accepted for publication in MNRAS. To get a copy
with high-resolution figures, use the web interface, or download the
Identikit software, visit
http://www.ifa.hawaii.edu/faculty/barnes/research/identikit
Topological fractal networks introduced by mixed degree distribution
Several fundamental properties of real complex networks, such as the
small-world effect, the scale-free degree distribution, and recently discovered
topological fractal structure, have presented the possibility of a unique
growth mechanism and allow for uncovering universal origins of collective
behaviors. However, highly clustered scale-free network, with power-law degree
distribution, or small-world network models, with exponential degree
distribution, are not self-similarity. We investigate networks growth mechanism
of the branching-deactivated geographical attachment preference that learned
from certain empirical evidence of social behaviors. It yields high clustering
and spectrums of degree distribution ranging from algebraic to exponential,
average shortest path length ranging from linear to logarithmic. We observe
that the present networks fit well with small-world graphs and scale-free
networks in both limit cases (exponential and algebraic degree distribution
respectively), obviously lacking self-similar property under a length-scale
transformation. Interestingly, we find perfect topological fractal structure
emerges by a mixture of both algebraic and exponential degree distributions in
a wide range of parameter values. The results present a reliable connection
among small-world graphs, scale-free networks and topological fractal networks,
and promise a natural way to investigate universal origins of collective
behaviors.Comment: 14 pages, 6 figure
An evolutionary behavioral model for decision making
For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud
of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud
the same evolutionary mechanism is able to assemble quite\ud
complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies
Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks
We outline a possible theoretical framework for the quantitative modeling of
networked embodied cognitive systems. We notice that: 1) information self
structuring through sensory-motor coordination does not deterministically occur
in Rn vector space, a generic multivariable space, but in SE(3), the group
structure of the possible motions of a body in space; 2) it happens in a
stochastic open ended environment. These observations may simplify, at the
price of a certain abstraction, the modeling and the design of self
organization processes based on the maximization of some informational
measures, such as mutual information. Furthermore, by providing closed form or
computationally lighter algorithms, it may significantly reduce the
computational burden of their implementation. We propose a modeling framework
which aims to give new tools for the design of networks of new artificial self
organizing, embodied and intelligent agents and the reverse engineering of
natural ones. At this point, it represents much a theoretical conjecture and it
has still to be experimentally verified whether this model will be useful in
practice.
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
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