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276 results on '"Neri, Filippo"'

2. Mapping Learning Algorithms on Data, a useful step for optimizing performances and their comparison

Author

Neri, Filippo

Subjects
Computer Science - Machine Learning
Abstract

In the paper, we propose a novel methodology to map learning algorithms on data (performance map) in order to gain more insights in the distribution of their performances across their parameter space. This methodology provides useful information when selecting a learner's best configuration for the data at hand, and it also enhances the comparison of learners across learning contexts. In order to explain the proposed methodology, the study introduces the notions of learning context, performance map, and high performance function. It then applies these concepts to a variety of learning contexts to show how their use can provide more insights in a learner's behavior, and can enhance the comparison of learners across learning contexts. The study is completed by an extensive experimental study describing how the proposed methodology can be applied., Comment: The main classification class for the paper is Machine Learning

Published
2021

4. Domain Specific Concept Drift Detectors for Predicting Financial Time Series

Author

Neri, Filippo

Subjects
Quantitative Finance - Statistical Finance, Computer Science - Machine Learning
Abstract

Concept drift detectors allow learning systems to maintain good accuracy on non-stationary data streams. Financial time series are an instance of non-stationary data streams whose concept drifts (market phases) are so important to affect investment decisions worldwide. This paper studies how concept drift detectors behave when applied to financial time series. General results are: a) concept drift detectors usually improve the runtime over continuous learning, b) their computational cost is usually a fraction of the learning and prediction steps of even basic learners, c) it is important to study concept drift detectors in combination with the learning systems they will operate with, and d) concept drift detectors can be directly applied to the time series of raw financial data and not only to the model's accuracy one. Moreover, the study introduces three simple concept drift detectors, tailored to financial time series, and shows that two of them can be at least as effective as the most sophisticated ones from the state of the art when applied to financial time series. Currently submitted to Pattern Recognition

Published
2021

5. How to Identify Investor's types in real financial markets by means of agent based simulation

Author

Neri, Filippo

Subjects
Quantitative Finance - Trading and Market Microstructure, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, I.2.6
Abstract

The paper proposes a computational adaptation of the principles underlying principal component analysis with agent based simulation in order to produce a novel modeling methodology for financial time series and financial markets. Goal of the proposed methodology is to find a reduced set of investor s models (agents) which is able to approximate or explain a target financial time series. As computational testbed for the study, we choose the learning system L FABS which combines simulated annealing with agent based simulation for approximating financial time series. We will also comment on how L FABS s architecture could exploit parallel computation to scale when dealing with massive agent simulations. Two experimental case studies showing the efficacy of the proposed methodology are reported., Comment: 18 pages, in press

Published
2020

6. Combining Machine Learning and Agent Based Modeling for Gold Price Prediction

Author

Neri, Filippo, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Cagnoni, Stefano, editor, Mordonini, Monica, editor, Pecori, Riccardo, editor, Roli, Andrea, editor, and Villani, Marco, editor

Published
2019
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7. PID Tuning with Neural Networks

Author

Marino, Antonio, Neri, Filippo, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Nguyen, Ngoc Thanh, editor, Gaol, Ford Lumban, editor, Hong, Tzung-Pei, editor, and Trawiński, Bogdan, editor

Published
2019
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8. Case Study on Modeling the Silver and Nasdaq Financial Time Series with Simulated Annealing

Author

Neri, Filippo, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Álvaro, editor, Adeli, Hojjat, editor, Reis, Luís Paulo, editor, and Costanzo, Sandra, editor

Published
2018
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9. Quantum interference and Bayes theorem

Author

Neri, Filippo

Subjects
Quantum Physics
Abstract

This note is a comment on the "quantum interferometry" section of Reference [1]. It points out that the methods of that section can be applied to more general states than the ones that are discussed in Ref. [1]., Comment: 5 pages, no figure

Published
2006

10. Quantum Bayesian methods and subsequent measurements

Author

Neri, Filippo

Subjects
Quantum Physics
Abstract

After a derivation of the quantum Bayes theorem, and a discussion of the reconstruction of the unknown state of identical spin systems by repeated measurements, the main part of this paper treats the problem of determining the unknown phase difference of two coherent sources by photon measurements. While the approach of this paper is based on computing correlations of actual measurements (photon detections), it is possible to derive indirectly a probability distribution for the phase difference. In this approach, the quantum phase is not an observable, but a parameter of an unknown quantum state. Photon measurements determine a probability distribution for the phase difference. The approach used in this paper takes into account both photon statistics and the finite efficiency of the detectors., Comment: Expanded and corrected version. 13 pages, 1 figure

Published
2005
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12. Modelling Physics Knowledge Acquisition in Children with Machine Learning

Author

Neri, Filippo, Saitta, Lorenza, and Tiberghien, Andree

Abstract

A computational approach to the simulation of cognitive modelling of children learning elementary physics is presented. Goal of the simulation is to support the cognitive scientist's investigation of learning in humans. The Machine Learning system WHY , able to handle domain knowledge (including a causal model of the domain), has been chosen as tool for the simulation of the cognitive development. In this paper the focus will be on knowledge representation schemes, useful to support further modelling of conceptual change.

Published
1997

15. Learning Predictive Models for Financial Time Series by Using Agent Based Simulations

Author

Neri, Filippo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Nguyen, Ngoc Thanh, editor

Published
2012
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16. A Comparative Study of a Financial Agent Based Simulator Across Learning Scenarios

Author

Neri, Filippo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Cao, Longbing, editor, Bazzan, Ana L. C., editor, Symeonidis, Andreas L., editor, Gorodetsky, Vladimir I., editor, Weiss, Gerhard, editor, and Yu, Philip S., editor

Published
2012
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17. Learning and Predicting Financial Time Series by Combining Natural Computation and Agent Simulation

Author

Neri, Filippo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Di Chio, Cecilia, editor, Brabazon, Anthony, editor, Di Caro, Gianni A., editor, Drechsler, Rolf, editor, Farooq, Muddassar, editor, Grahl, Jörn, editor, Greenfield, Gary, editor, Prins, Christian, editor, Romero, Juan, editor, Squillero, Giovanni, editor, Tarantino, Ernesto, editor, Tettamanzi, Andrea G. B., editor, Urquhart, Neil, editor, and Uyar, A. Şima, editor

Published
2011
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33. INTRODUZIONE E SCOPO DEL LAVORO

Author

P Salera, A Pelliccetti, S Pierandrei, G Perrone, V V Gomes, M Vitillo, U O C Patologia Clinica, U O C Anatomia Patologica, Ospedale San, Neri, Filippo, and Asl Roma

Published
2021
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45. Exploring the power of genetic search in learning symbolic classifiers

Author

Neri, Filippo and Saitta, Lorenza

Subjects
Machine learning -- Research, Machine vision -- Research, Automatic classification -- Research
Abstract

In this paper we show, in a constructive way, that there are problems for which the use of genetic algorithm based learning systems can be at least as effective as traditional symbolic or connectionist approaches. To this aim, the system REGAL is briefly described, and its application to two classical benchmarks for Machine Learning is discussed, by comparing the results with the best ones published in the literature. Index Terms - Genetic algorithms, distributed genetic algorithms, classification rules, machine learning, disjunctive concept learning, universal suffrage selection, mushroom dataset, splice junctions dataset, empirical comparison.

Published
1996

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