Littérature scientifique sur le sujet « Algoritmi di apprendimento automatico »
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Articles de revues sur le sujet "Algoritmi di apprendimento automatico"
Lahmann, Henning, et Robin Geiß. « The use of AI in military contexts : opportunities and regulatory challenges ». Military Law and the Law of War Review 59, no 2 (19 janvier 2022) : 165–95. http://dx.doi.org/10.4337/mllwr.2021.02.02.
Texte intégralMichelini, Samanta, Simon Tscholl, Johannes Erschbamer, Daniel Plaikner, Lukas Egarter Vigl et Walter Guerra. « KULTIVAS : studio di fattibilità di un modello sito-varietale per la melicoltura ». Laimburg Journal 4 (27 septembre 2022). http://dx.doi.org/10.23796/lj/2022.008.
Texte intégralThèses sur le sujet "Algoritmi di apprendimento automatico"
Tullo, Alessandra. « Apprendimento automatico con metodo kernel ». Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23200/.
Texte intégralGiavoli, Andrea. « Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time : Adattamento di un algoritmo di apprendimento automatico per la caratterizzazione e la ricerca di frequent patterns su macchine automatiche ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9055/.
Texte intégralStefenelli, Marco. « Apprendimento automatico nei giochi di strategia ». Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7660/.
Texte intégralUgolini, Matilde. « Metodologie di apprendimento automatico applicate alla generazione di dati 3D ». Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10415/.
Texte intégralSPALLANZANI, MATTEO. « Un framework per l’analisi dei sistemi di apprendimento automatico ». Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2020. http://hdl.handle.net/11380/1200571.
Texte intégralMaking predictions is about getting insights into the patterns of our environment. We can access the physical world through media, measuring instruments, which provide us with data in which we hope to find useful patterns. The development of computing machines has allowed storing large data sets and processing them at high speed. Machine learning studies systems which can automate the detection of patterns in large data sets using computers. Machine learning lies at the core of data science and artificial intelligence, two research fields which are changing the economy and the society in which we live. Machine learning systems are usually trained and deployed on powerful computer clusters composed by hundreds or thousands of machines. Nowadays, the miniaturisation of computing devices is allowing deploying them on battery-powered systems embedded into diverse environments. With respect to computer clusters, these devices are far less powerful, but have the advantage of being nearer to the source of the data. On one side, this increases the number of applications of machine learning systems; on the other side, the physical limitations of the computing machines require identifying proper metrics to assess the fitness of different machine learning systems in a given context. In particular, these systems should be evaluated according not only to their modelling and statistical properties, but also to their algorithmic costs and their fitness to different computer architectures. In this thesis, we analyse modelling, algorithmic and architectural properties of different machine learning systems. We present the fingerprint method, a system which was developed to solve a business intelligence problem where statistical accuracy was more important than latency or energy constraints. Then, we analyse artificial neural networks and discuss their appealing computational properties; we also describe an example application, a model we designed to identify the objective causes of subjective driving perceptions. Finally, we describe and analyse quantized neural networks, artificial neural networks which use finite sets for the parameters and step activation functions. These limitations pose challenging mathematical problems, but quantized neural networks can be executed extremely efficiently on dedicated hardware accelerators, making them ideal candidates to deploy machine learning on edge computers. In particular, we show that quantized neural networks are equivalent to classical artificial neural networks (at least on the set of targets represented by continuous functions defined on compact domains); we also present a novel gradient-based learning algorithm for, named additive noise annealing, based on the regularisation effect of additive noise on the argument of discontinuous functions, reporting state-of-the-art results on image classification benchmarks.
TOMEI, MATTEO. « Riconoscimento di azioni nei video tramite tecnologie computazionali, multimediali e di apprendimento automatico ». Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2022. http://hdl.handle.net/11380/1271188.
Texte intégralVideo clips represent the most pervasive means of disseminating information nowadays. With their outbreak, needs for automatic categorization and content understanding have also increased, both for entertainment purposes and professional ones. In the context of multimedia and deep learning technologies for video comprehension, we explore and devise video-based algorithms and state-of-the-art solutions to tackle action recognition and fine-grained action localization. Our research is not limited to the quantitative evaluation of the proposed approaches for improving performance on specific tasks. We observe that handling video content usually brings some drawbacks. Videos often involve human actors and could arise privacy issues that are not yet sufficiently investigated by the computer vision community. Moreover, given their complexity and variability, videos are not easy to process and often require large computational resources. In addition to the application scenario, this thesis tackles two main challenges related to automatic video processing, namely privacy issues and computation. In the application part, we investigate the simultaneous detection of multiple actors and the classification of their actions, by exploiting interactions between people and surrounding objects, both in space and time. We also explore a more production-oriented application, in collaboration with Metaliquid SRL and in line with the company’s needs, by devising a deep network for salient action spotting in broadcast soccer matches. Regarding the privacy issue, we propose a novel strategy for masking people’s identities in video clips while preserving the ability of action recognition models to predict correct class labels. Finally, from the computational perspective, we develop an algorithm for reducing the size and resource utilization of existing deep neural networks, while keeping performances. These three aspects of video modeling are investigated separately but have proved to be generalizable, making it easier to build efficient and privacy-preserving action recognition models. All the alternatives and solutions presented in this work build upon deep learning, requiring a huge amount of data for learning video representations.
Bartolini, Manuel. « Sviluppo di algoritmi per l'automazione di misure industriali ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3282/.
Texte intégralNigri, Simone. « Ottimizzatore per configurazione automatica di algoritmi di pattern matching ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Trouver le texte intégralLETTERI, IVAN. « Strategie di Miglioramento delle Prestazioni per rilevamento del traffico di malware con Modelli di Apprendimento Automatico ». Doctoral thesis, Università degli Studi dell'Aquila, 2020. http://hdl.handle.net/11697/163416.
Texte intégralTeci, Marco. « Implementazione e verifica degli algoritmi per il controllo di esposizione automatico nelle radiografie ». Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6403/.
Texte intégralLivres sur le sujet "Algoritmi di apprendimento automatico"
Apprendimento Facili Strutture Dati e Algoritmi JavaScript : Strutture Di Dati e Algoritmi Classici in JavaScript. Independently Published, 2020.
Trouver le texte intégralhu, yang. Apprendimento Facili Strutture Dati e Algoritmi C# : Apprendi Graficamente Strutture e Algoritmi Di Dati C# Meglio Di Prima. Independently Published, 2020.
Trouver le texte intégralApprendimento Facili Strutture Dati e Algoritmi C# : Apprendi Graficamente Strutture e Algoritmi Di Dati C# Meglio Di Prima. Independently Published, 2021.
Trouver le texte intégralMachine Learning con Python : Guida Base Alle Tecniche Di Apprendimento Automatico. Independently Published, 2021.
Trouver le texte intégralApprendimento Facili Strutture Dati e Algoritmi Go : Impara Graficamente le Strutture Dati e gli Algoritmi Di Go Meglio Di Prima. Independently Published, 2020.
Trouver le texte intégralApprendimento Facili Strutture Dati e Algoritmi Java : Impara Strutture e Algoritmi Di Dati in Modo Grafico e Semplice. Independently Published, 2020.
Trouver le texte intégralhu, yang. Apprendimento Facili Strutture Dati e Algoritmi Python 3 : Impara Graficamente le Strutture Dati e gli Algoritmi Di Python 3 Meglio Di Prima. Independently Published, 2020.
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