27,390 research outputs found
Buffered Reset Leads to Improved Compression in Fuzzy ARTMAP Classification of Radar Range Profiles
Fuzzy ARTMAP has to date been applied to a variety of automatic target recognition tasks, including radar range profile classification. In simulations of this task, it has demonstrated significant compression compared to k-nearest-neighbor classifiers. During supervised learning, match tracking search allocates memory based on the degree of similarity between newly encountered and previously encountered inputs, regardless of their prior predictive success. Here we invesetigate techniques that buffer reset based on a category's previous predictive success and thereby substantially improve the compression achieved with minimal loss of accuracy.Office of Naval Research (N00014-95-1-0657, N00014-95-1-0409, N00014-96-1-0659
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model
Vertex centrality measures are a multi-purpose analysis tool, commonly used
in many application environments to retrieve information and unveil knowledge
from the graphs and network structural properties. However, the algorithms of
such metrics are expensive in terms of computational resources when running
real-time applications or massive real world networks. Thus, approximation
techniques have been developed and used to compute the measures in such
scenarios. In this paper, we demonstrate and analyze the use of neural network
learning algorithms to tackle such task and compare their performance in terms
of solution quality and computation time with other techniques from the
literature. Our work offers several contributions. We highlight both the pros
and cons of approximating centralities though neural learning. By empirical
means and statistics, we then show that the regression model generated with a
feedforward neural networks trained by the Levenberg-Marquardt algorithm is not
only the best option considering computational resources, but also achieves the
best solution quality for relevant applications and large-scale networks.
Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models,
Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv
admin note: text overlap with arXiv:1810.1176
Using neural networks in software repositories
The first topic is an exploration of the use of neural network techniques to improve the effectiveness of retrieval in software repositories. The second topic relates to a series of experiments conducted to evaluate the feasibility of using adaptive neural networks as a means of deriving (or more specifically, learning) measures on software. Taken together, these two efforts illuminate a very promising mechanism supporting software infrastructures - one based upon a flexible and responsive technology
Boosting Applied to Word Sense Disambiguation
In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied
to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of
15 selected polysemous words show that the boosting approach surpasses Naive
Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy
on supervised WSD. In order to make boosting practical for a real learning
domain of thousands of words, several ways of accelerating the algorithm by
reducing the feature space are studied. The best variant, which we call
LazyBoosting, is tested on the largest sense-tagged corpus available containing
192,800 examples of the 191 most frequent and ambiguous English words. Again,
boosting compares favourably to the other benchmark algorithms.Comment: 12 page
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