5,213 research outputs found
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
Machine learning from coronas using parametrization of images
We were interested to develop an algorithm for detection of coronas of people in altered states of consciousness (two-classes problem). Such coronas are known to have rings (double coronas), special branch-like structure of streamers and/or curious spots. We used several approaches to parametrization of images and various machine learning algorithms. We compared results of computer algorithms with the human expert’s accuracy. Results show that computer algorithms can achieve the same or even better accuracy than that of human experts
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Automatic Environmental Sound Recognition: Performance versus Computational Cost
In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost
Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications
At present, decision making solutions developed based on deep learning (DL)
models have received extensive attention in predictive maintenance (PM)
applications along with the rapid improvement of computing power. Relying on
the superior properties of shared weights and spatial pooling, Convolutional
Neural Network (CNN) can learn effective representations of health states from
industrial data. Many developed CNN-based schemes, such as advanced CNNs that
introduce residual learning and multi-scale learning, have shown good
performance in health state recognition tasks under the assumption that all the
classes are known. However, these schemes have no ability to deal with new
abnormal samples that belong to state classes not part of the training set. In
this paper, a collective decision framework for different CNNs is proposed. It
is based on a One-vs-Rest network (OVRN) to simultaneously achieve
classification of known and unknown health states. OVRN learn state-specific
discriminative features and enhance the ability to reject new abnormal samples
incorporated to different CNNs. According to the validation results on the
public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based
decision schemes incorporating OVRN have outstanding recognition ability for
samples of unknown heath states, while maintaining satisfactory accuracy on
known states. The results show that the new DL framework outperforms
conventional CNNs, and the one based on residual and multi-scale learning has
the best overall performance
A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs
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