6,708 research outputs found
From Data Topology to a Modular Classifier
This article describes an approach to designing a distributed and modular
neural classifier. This approach introduces a new hierarchical clustering that
enables one to determine reliable regions in the representation space by
exploiting supervised information. A multilayer perceptron is then associated
with each of these detected clusters and charged with recognizing elements of
the associated cluster while rejecting all others. The obtained global
classifier is comprised of a set of cooperating neural networks and completed
by a K-nearest neighbor classifier charged with treating elements rejected by
all the neural networks. Experimental results for the handwritten digit
recognition problem and comparison with neural and statistical nonmodular
classifiers are given
Hierarchy in Gene Expression is Predictive for Adult Acute Myeloid Leukemia
Cancer progresses with a change in the structure of the gene network in
normal cells. We define a measure of organizational hierarchy in gene networks
of affected cells in adult acute myeloid leukemia (AML) patients. With a
retrospective cohort analysis based on the gene expression profiles of 116
acute myeloid leukemia patients, we find that the likelihood of future cancer
relapse and the level of clinical risk are directly correlated with the level
of organization in the cancer related gene network. We also explore the
variation of the level of organization in the gene network with cancer
progression. We find that this variation is non-monotonic, which implies the
fitness landscape in the evolution of AML cancer cells is nontrivial. We
further find that the hierarchy in gene expression at the time of diagnosis may
be a useful biomarker in AML prognosis.Comment: 18 pages, 5 figures, to appear in Physical Biolog
Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements
A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR)
Corporate payments networks and credit risk rating
Aggregate and systemic risk in complex systems are emergent phenomena
depending on two properties: the idiosyncratic risks of the elements and the
topology of the network of interactions among them. While a significant
attention has been given to aggregate risk assessment and risk propagation once
the above two properties are given, less is known about how the risk is
distributed in the network and its relations with the topology. We study this
problem by investigating a large proprietary dataset of payments among 2.4M
Italian firms, whose credit risk rating is known. We document significant
correlations between local topological properties of a node (firm) and its
risk. Moreover we show the existence of an homophily of risk, i.e. the tendency
of firms with similar risk profile to be statistically more connected among
themselves. This effect is observed when considering both pairs of firms and
communities or hierarchies identified in the network. We leverage this
knowledge to show the predictability of the missing rating of a firm using only
the network properties of the associated node
Third Conference on Artificial Intelligence for Space Applications, part 2
Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed
A neutrino interaction with two vertices topology detected by OPERA
The OPERA experiment has reported the detection of five
candidates in the CNGS beam, allowing to reject the background-only
hypothesis at the 5.1 level. Besides these events, on May 23
2011, OPERA detected a "neutral current like" interaction with two secondary
vertices. Such topologies mainly arise from Charged Current interactions of a
with associated charm quark production or from Neutral Current
interactions of a with production of a charm anti-charm pair. These
topologies have generally low probabilities. A dedicated multivariate analysis
is in progress to allow discriminating between these two hypotheses. Here the
event topology is described in detail and preliminary results of the
classifiers for all possible contributions are given.Comment: Talk presented at NuPhys2015 (London, 16-18 December 2015). 4 pages,
LaTeX, 2 eps figure
Deep Information Networks
We describe a novel classifier with a tree structure, designed using
information theory concepts. This Information Network is made of information
nodes, that compress the input data, and multiplexers, that connect two or more
input nodes to an output node. Each information node is trained, independently
of the others, to minimize a local cost function that minimizes the mutual
information between its input and output with the constraint of keeping a given
mutual information between its output and the target (information bottleneck).
We show that the system is able to provide good results in terms of accuracy,
while it shows many advantages in terms of modularity and reduced complexity
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