Pinned Repositories
bootstrap-transfer
This is a two-column transfer multi-select widget inspired by Django admin module's similar widget.
C10
C10 is a strongly-typed, object-oriented, probabilistic, timed, concurrent constraint programming language, being developed within the X10 family of languages.
Cpnet-dist
lcp
lessons
sentag
Tesi triennale Alberto Zerbinati, Simone Mosco. Relatore prof. Andrea Loreggia. @unipd
SEP-net
sofai
This code has been generated by Andrea Loreggia in the context of an IBM-led project on Thinking Fast and Slow in AI
test
vorace
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique that does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.
aloreggia's Repositories
aloreggia/vorace
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique that does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.
aloreggia/Cpnet-dist
aloreggia/SEP-net
aloreggia/sofai
This code has been generated by Andrea Loreggia in the context of an IBM-led project on Thinking Fast and Slow in AI
aloreggia/bootstrap-transfer
This is a two-column transfer multi-select widget inspired by Django admin module's similar widget.
aloreggia/C10
C10 is a strongly-typed, object-oriented, probabilistic, timed, concurrent constraint programming language, being developed within the X10 family of languages.
aloreggia/lcp
aloreggia/lessons
aloreggia/sentag
Tesi triennale Alberto Zerbinati, Simone Mosco. Relatore prof. Andrea Loreggia. @unipd
aloreggia/test