9,689 research outputs found
Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification
[Abriged] Astronomical Wide Field Imaging performed with new large format CCD
detectors poses data reduction problems of unprecedented scale which are
difficult to deal with traditional interactive tools. We present here NExt
(Neural Extractor): a new Neural Network (NN) based package capable to detect
objects and to perform both deblending and star/galaxy classification in an
automatic way. Traditionally, in astronomical images, objects are first
discriminated from the noisy background by searching for sets of connected
pixels having brightnesses above a given threshold and then they are classified
as stars or as galaxies through diagnostic diagrams having variables choosen
accordingly to the astronomer's taste and experience. In the extraction step,
assuming that images are well sampled, NExt requires only the simplest a priori
definition of "what an object is" (id est, it keeps all structures composed by
more than one pixels) and performs the detection via an unsupervised NN
approaching detection as a clustering problem which has been thoroughly studied
in the artificial intelligence literature. In order to obtain an objective and
reliable classification, instead of using an arbitrarily defined set of
features, we use a NN to select the most significant features among the large
number of measured ones, and then we use their selected features to perform the
classification task. In order to optimise the performances of the system we
implemented and tested several different models of NN. The comparison of the
NExt performances with those of the best detection and classification package
known to the authors (SExtractor) shows that NExt is at least as effective as
the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at
http://www.na.astro.it/~andreon/listapub.htm
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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On the adequacy of current empirical evaluations of formal models of categorization
Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)
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