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Advances in Neural Networks

Part of the Smart Innovation, Systems and Technologies Book Series (volume 54)

Editors: Bassis, Simone; Esposito, Anna; Morabito, Francesco Carlo; Pasero, Eros

DOI: 10.1007/978-3-319-33747-0

ISBN: 978-3-319-33746-3

Publisher: Springer International Publishing - Springer Journals

Jun 19, 2016

1 - 10 of 52 Chapters

LitStream

Some Notes on Computational and Theoretical Issues in Artificial Intelligence and Machine Learning

Esposito, Anna; Bassis, Simone; Morabito, Francesco Carlo; Pasero, Eros

Pages: 3 –12

[In the attempt to implement applications of public utility which simplify the user access to future, remote and nearby social services, new mathematical models and new psychological and computational approaches from existing cognitive frameworks and algorithmic solutions have been developed. The...
LitStream

Semi-automatic Brain Lesion Segmentation in Gamma Knife Treatments Using an Unsupervised Fuzzy C-Means Clustering Technique

Rundo, Leonardo; Militello, Carmelo; Vitabile, Salvatore; Russo, Giorgio; Pisciotta, Pietro; Marletta, Francesco; Ippolito, Massimo; D’Arrigo, Corrado; Midiri, Massimo; Gilardi, Maria Carla

Pages: 15 –26

[MR Imaging is being increasingly used in radiation treatment planning as well as for staging and assessing tumor response. Leksell Gamma Knife® is a device for stereotactic neuro-radiosurgery to deal with inaccessible or insufficiently treated lesions with traditional surgery or radiotherapy....
LitStream

Machine Learning Based Data Mining for Milky Way Filamentary Structures Reconstruction

Riccio, Giuseppe; Cavuoti, Stefano; Schisano, Eugenio; Brescia, Massimo; Mercurio, Amata; Elia, Davide; Benedettini, Milena; Pezzuto, Stefano; Molinari, Sergio; Di Giorgio, Anna Maria

Pages: 27 –36

[We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux...
LitStream

3-D Hand Pose Estimation from Kinect’s Point Cloud Using Appearance Matching

Coscia, Pasquale; Palmieri, Francesco A. N.; Castaldo, Francesco; Cavallo, Alberto

Pages: 37 –45

[We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different...
LitStream

Frequency-Domain Adaptive Filtering in Hypercomplex Systems

Ortolani, Francesca; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio

Pages: 47 –56

[In recent years, linear and nonlinear signal processing applications required the development of new multidimensional algorithms. Higher-dimensional algorithms include quaternion-valued filters. One of the drawbacks filter designers have to cope with is the increasing computational cost due to...
LitStream

Bayesian Clustering on Images with Factor Graphs in Reduced Normal Form

Buonanno, Amedeo; di Grazia, Luigi; Palmieri, Francesco A. N.

Pages: 57 –65

[Bayesian clustering implemented on a small Factor Graph is utilized in this work to perform associative recall and pattern recognition on images. The network is trained using a maximum likelihood algorithm on images from a standard data set. The two-class labels are fused with the image data...
LitStream

Selection of Negative Examples for Node Label Prediction Through Fuzzy Clustering Techniques

Frasca, Marco; Malchiodi, Dario

Pages: 67 –76

[Negative examples, which are required for most machine learning methods to infer new predictions, are rarely directly recorded in several real world databases for classification problems. A variety of heuristics for the choice of negative examples have been proposed, ranging from simply...
LitStream

Recent Advances on Distributed Unsupervised Learning

Rosato, Antonello; Altilio, Rosa; Panella, Massimo

Pages: 77 –86

[Distributed machine learning is a problem of inferring a desired relation when the training data is distributed throughout a network of agents (e.g. sensor networks, robot swarms, etc.). A typical problem of unsupervised learning is clustering, that is grouping patterns based on some...
LitStream

A Rule Based Recommender System

Apolloni, Bruno; Bassis, Simone; Mesiti, Marco; Valtolina, Stefano; Epifania, Francesco

Pages: 87 –96

[We introduce a new recommending paradigm based on the genomic features of the candidate objects. The system is based on the tree structure of the object metadata which we convert in acceptance rules, leaving the user the discretion of selecting the most convincing rules for her/his scope. We...
LitStream

Learning from Nowhere

Apolloni, Bruno; Bassis, Simone; Rota, Jacopo; Galliani, Gian Luca; Gioia, Matteo; Ferrari, Luca

Pages: 97 –109

[We extend the Fuzzy Inference System (FIS) paradigm to the case where the universe of discourse is hidden to the learning algorithm. Hence the training set is constituted by a set of fuzzy attributes in whose correspondence some consequents are observed. The scenario is further complicated by...

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