105,578 research outputs found
The Best Trail Algorithm for Assisted Navigation of Web Sites
We present an algorithm called the Best Trail Algorithm, which helps solve
the hypertext navigation problem by automating the construction of memex-like
trails through the corpus. The algorithm performs a probabilistic best-first
expansion of a set of navigation trees to find relevant and compact trails. We
describe the implementation of the algorithm, scoring methods for trails,
filtering algorithms and a new metric called \emph{potential gain} which
measures the potential of a page for future navigation opportunities.Comment: 11 pages, 11 figure
Reduced Complexity Sphere Decoding
In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can
achieve performance equivalent to full search Maximum Likelihood (ML) decoding,
with reduced complexity. Several researchers reported techniques that reduce
the complexity of SD further. In this paper, a new technique is introduced
which decreases the computational complexity of SD substantially, without
sacrificing performance. The reduction is accomplished by deconstructing the
decoding metric to decrease the number of computations and exploiting the
structure of a lattice representation. Furthermore, an application of SD,
employing a proposed smart implementation with very low computational
complexity is introduced. This application calculates the soft bit metrics of a
bit-interleaved convolutional-coded MIMO system in an efficient manner. Based
on the reduced complexity SD, the proposed smart implementation employs the
initial radius acquired by Zero-Forcing Decision Feedback Equalization (ZF-DFE)
which ensures no empty spheres. Other than that, a technique of a particular
data structure is also incorporated to efficiently reduce the number of
executions carried out by SD. Simulation results show that these approaches
achieve substantial gains in terms of the computational complexity for both
uncoded and coded MIMO systems.Comment: accepted to Journal. arXiv admin note: substantial text overlap with
arXiv:1009.351
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
K-nearest Neighbor Search by Random Projection Forests
K-nearest neighbor (kNN) search has wide applications in many areas,
including data mining, machine learning, statistics and many applied domains.
Inspired by the success of ensemble methods and the flexibility of tree-based
methodology, we propose random projection forests (rpForests), for kNN search.
rpForests finds kNNs by aggregating results from an ensemble of random
projection trees with each constructed recursively through a series of
carefully chosen random projections. rpForests achieves a remarkable accuracy
in terms of fast decay in the missing rate of kNNs and that of discrepancy in
the kNN distances. rpForests has a very low computational complexity. The
ensemble nature of rpForests makes it easily run in parallel on multicore or
clustered computers; the running time is expected to be nearly inversely
proportional to the number of cores or machines. We give theoretical insights
by showing the exponential decay of the probability that neighboring points
would be separated by ensemble random projection trees when the ensemble size
increases. Our theory can be used to refine the choice of random projections in
the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc
Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context
A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA
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