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Displaying 1-18 out of 18 total
Using the Case-Based Ranking Methodology for Test Case Prioritization
Found in: Software Maintenance, IEEE International Conference on
By Paolo Tonella, Paolo Avesani, Angelo Susi
Issue Date:September 2006
pp. 123-133
<p>The test case execution order affects the time at which the objectives of testing are met. If the objective is fault detection, an inappropriate execution order might reveal most faults late, thus delaying the bug fixing activity and eventually th...
User-centric Affective Video Tagging from MEG and Peripheral Physiological Responses
Found in: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)
By Mojtaba Khomami Abadi,Seyed Mostafa Kia,Ramanathan Subramanian,Paolo Avesani,Nicu Sebe
Issue Date:September 2013
pp. 582-587
This paper presents a new multimodal database and the associated results for characterization of affect (valence, arousal and dominance) using the Magneto encephalogram (MEG) brain signals and peripheral physiological signals (horizontal EOG, ECG, trapeziu...
A Machine Learning Approach to Software Requirements Prioritization
Found in: IEEE Transactions on Software Engineering
By Anna Perini,Angelo Susi,Paolo Avesani
Issue Date:April 2013
pp. 445-461
Deciding which, among a set of requirements, are to be considered first and in which order is a strategic process in software development. This task is commonly referred to as requirements prioritization. This paper describes a requirements prioritization ...
Testing Multiclass Pattern Discrimination
Found in: 2012 2nd International Workshop on Pattern Recognition in NeuroImaging (PRNI)
By Emanuele Olivetti,Susanne Greiner,Paolo Avesani
Issue Date:July 2012
pp. 57-60
Machine learning is increasingly adopted in neuroimaging-based neuroscience studies. The paradigm of predicting the stimuli provided to the subject from the concurrent brain activity is known as "brain decoding" and accurate predictions support t...
Testing for Information with Brain Decoding
Found in: Pattern Recognition in NeuroImaging, IEEE International Workshop on
By Emanuele Olivetti, Sriharsha Veeramachaneni, Paolo Avesani
Issue Date:May 2011
pp. 33-36
Is there information about the stimulus given to the subject within brain data? The brain decoding approach tries to answer this question by means of machine learning algorithms. A classifier is learned from a small sample of brain data that is class-label...
Learning BOLD Response in fMRI by Reservoir Computing
Found in: Pattern Recognition in NeuroImaging, IEEE International Workshop on
By Paolo Avesani, Hananel Hazan, Ester Koilis, Larry Manevitz, Diego Sona
Issue Date:May 2011
pp. 57-60
This work proposes a model-free approach to fMRI-based brain mapping where the BOLD response is learnt from data rather than assumed in advance. For each voxel, a paired sequence of stimuli and fMRI recording is given to a supervised learning process. The ...
Multivariate Brain Mapping by Random Subspaces
Found in: Pattern Recognition, International Conference on
By Diego Sona, Paolo Avesani
Issue Date:August 2010
pp. 2576-2579
Functional neuroimaging consists in the use of imaging technologies allowing to record the functional brain activity in real-time. Among all techniques, data produced by functional magnetic resonance is encoded as sequences of 3D images of thousands of vox...
Brain decoding: Biases in error estimation
Found in: 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD 2010)
By Emanuele Olivetti, Andrea Mognon, Susanne Greiner, Paolo Avesani
Issue Date:August 2010
pp. 40-43
Classification-based approaches for data analysis are provoking wide interest and increasing adoption within the neuroscience community. Topics like
Facing Scalability Issues in Requirements Prioritization with Machine Learning Techniques
Found in: Requirements Engineering, IEEE International Conference on
By Paolo Avesani, Cinzia Bazzanella, Anna Perini, Angelo Susi
Issue Date:September 2005
pp. 297-306
<p>Case-based driven approaches to requirements prioritization proved to be much more effective than first-principle methods in being tailored to a specific problem, that is they take advantage of the implicit knowledge that is available, given a pro...
Active Sampling for Feature Selection
Found in: Data Mining, IEEE International Conference on
By Sriharsha Veeramachaneni, Paolo Avesani
Issue Date:November 2003
pp. 665
In knowledge discovery applications, where new features are to be added, an acquisition policy can help select the features to be acquired based on their relevance and the cost of extraction. This can be posed as a feature selection problem where the featu...
Advanced Metrics for Class-Driven Similarity Search
Found in: Database and Expert Systems Applications, International Workshop on
By Paolo Avesani, Enrico Blanzieri, Francesco Ricci
Issue Date:September 1999
pp. 223
This paper presents two metrics for the Nearest Neighbor Classifier that share the property of being adapted, i.e. learned, on a set of data. Both metrics can be used for similarity search when the retrieval critically depends on a symbolic target feature....
Data Compression and Local Metrics for Nearest Neighbor Classification
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Francesco Ricci, Paolo Avesani
Issue Date:April 1999
pp. 380-384
<p><b>Abstract</b>—A local distance measure for the nearest neighbor classification rule is shown to achieve high compression rates and high accuracy on real data sets. In the approach proposed here, first, a set of prototypes is extracte...
Decoding affect in videos employing the MEG brain signal
Found in: 2013 10th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2013)
By Mojtaba Khomami Abadi,Mostafa Kia,Ramanathan Subramanian,Paolo Avesani,Nicu Sebe
Issue Date:April 2013
pp. 1-6
This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram (MEG) brain signal. We attempt single-trial classification of movie and music videos with MEG responses extracted from seven participants. The main findings...
A trust-enhanced recommender system application: Moleskiing
Found in: Proceedings of the 2005 ACM symposium on Applied computing (SAC '05)
By Paolo Avesani, Paolo Massa, Roberto Tiella
Issue Date:March 2005
pp. 1589-1593
Recommender Systems (RS) suggests to users items they will like based on their past opinions. Collaborative Filtering (CF) is the most used technique to assess user similarity between users but very often the sparseness of user profiles prevents the comput...
Trust-aware recommender systems
Found in: Proceedings of the 2007 ACM conference on Recommender systems (RecSys '07)
By Paolo Avesani
Issue Date:October 2007
pp. 17-24
Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust me...
Active sampling for detecting irrelevant features
Found in: Proceedings of the 23rd international conference on Machine learning (ICML '06)
By Emanuele Olivetti, Paolo Avesani, Sriharsha Veeramachaneni
Issue Date:June 2006
pp. 961-968
The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the problem of choosing the unlabeled examples for which the class labels are qu...
Hierarchical Dirichlet model for document classification
Found in: Proceedings of the 22nd international conference on Machine learning (ICML '05)
By Diego Sona, Paolo Avesani, Sriharsha Veeramachaneni
Issue Date:August 2005
pp. 928-935
The proliferation of text documents on the web as well as within institutions necessitates their convenient organization to enable efficient retrieval of information. Although text corpora are frequently organized into concept hierarchies or taxonomies, th...
Shared lexicon for distributed annotations on the Web
Found in: Proceedings of the 14th international conference on World Wide Web (WWW '05)
By Marco Cova, Paolo Avesani
Issue Date:May 2005
pp. 207-214
The interoperability among distributed and autonomous systems is the ultimate challenge facing the semantic web. Heterogeneity of data representation is the main source of problems. This paper proposes an innovative solution that combines lexical approache...