arXiv subjects

Filippo Neri

Publications and source records attributed to Filippo Neri.

5 recordsLinked to original sources

arXiv2021

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

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.

cs.LG↗

arXiv2021

Domain Specific Concept Drift Detectors for Predicting Financial Time Series

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

q-fin.ST↗

arXiv2020

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

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.

q-fin.TR↗

arXiv2006

Quantum interference and Bayes theorem

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].

quant-ph↗

arXiv2005

Quantum Bayesian methods and subsequent measurements

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.

quant-ph↗