14,985 research outputs found
Pattern matching in high energy physics by using neural network and genetic algorithm
In this paper two different approaches to provide information from events by high energy physics experiments are shown. Usually the representations produced in such experiments are spot-composed and the classical algorithms to be needed for data analysis are time consuming. For this reason the possibility to speed up pattern recognition tasks by soft computing approach with parallel algorithms has been investigated. The first scheme shown in the following is a two-layer neural network with forward connections, the second one consists of an evolutionary algorithm with elitistic strategy and mutation and cross-over adaptive probability. Test results of these approaches have been carried out analysing a set of images produced by an optical ring imaging Cherenkov (RICH) detector at CERN
Structural graph matching using the EM algorithm and singular value decomposition
This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the EM algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows one to efficiently recover correspondence matches using the singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding method
Superpositional Quantum Network Topologies
We introduce superposition-based quantum networks composed of (i) the
classical perceptron model of multilayered, feedforward neural networks and
(ii) the algebraic model of evolving reticular quantum structures as described
in quantum gravity. The main feature of this model is moving from particular
neural topologies to a quantum metastructure which embodies many differing
topological patterns. Using quantum parallelism, training is possible on
superpositions of different network topologies. As a result, not only classical
transition functions, but also topology becomes a subject of training. The main
feature of our model is that particular neural networks, with different
topologies, are quantum states. We consider high-dimensional dissipative
quantum structures as candidates for implementation of the model.Comment: 10 pages, LaTeX2
Backpropagation training in adaptive quantum networks
We introduce a robust, error-tolerant adaptive training algorithm for
generalized learning paradigms in high-dimensional superposed quantum networks,
or \emph{adaptive quantum networks}. The formalized procedure applies standard
backpropagation training across a coherent ensemble of discrete topological
configurations of individual neural networks, each of which is formally merged
into appropriate linear superposition within a predefined, decoherence-free
subspace. Quantum parallelism facilitates simultaneous training and revision of
the system within this coherent state space, resulting in accelerated
convergence to a stable network attractor under consequent iteration of the
implemented backpropagation algorithm. Parallel evolution of linear superposed
networks incorporating backpropagation training provides quantitative,
numerical indications for optimization of both single-neuron activation
functions and optimal reconfiguration of whole-network quantum structure.Comment: Talk presented at "Quantum Structures - 2008", Gdansk, Polan
Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction
British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
- …