Search results for " Machine Learning"
showing 10 items of 300 documents
Non Linear Fitting Methods for Machine Learning
2017
This manuscript presents an analysis of numerical fitting methods used for solving classification problems as discriminant functions in machine learning. Non linear polynomial, exponential, and trigonometric models are mathematically deduced and discussed. Analysis about their pros and cons, and their mathematical modelling are made on what method to chose for what type of highly non linear multi-dimension problems are more suitable to be solved. In this study only deterministic models with analytic solutions are involved, or parameters calculation by numeric methods, which the complete model can subsequently be treated as a theoretical model. Models deduction are summarised and presented a…
Machine Learning: WEKA
2015
Cada vez más se utilizan ingentes cantidades de datos lo que conlleva la necesidad de extraer información útil para la toma de decisiones. La minería de datos es una tarea dentro de este proceso que utiliza la estadística como herramienta fundamental.
Potential and limitations of quantum extreme learning machines
2023
Quantum reservoir computers (QRC) and quantum extreme learning machines (QELM) aim to efficiently post-process the outcome of fixed -- generally uncalibrated -- quantum devices to solve tasks such as the estimation of the properties of quantum states. The characterisation of their potential and limitations, which is currently lacking, will enable the full deployment of such approaches to problems of system identification, device performance optimization, and state or process reconstruction. We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements, and provide an explicit characterisation of the information exactly retriev…
Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Specie…
2018
Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated …
A probabilistic approach to learning a visually grounded language model through human-robot interaction
2010
A Language is among the most fascinating and complex cognitive activities that develops rapidly since the early months of infants' life. The aim of the present work is to provide a humanoid robot with cognitive, perceptual and motor skills fundamental for the acquisition of a rudimentary form of language. We present a novel probabilistic model, inspired by the findings in cognitive sciences, able to associate spoken words with their perceptually grounded meanings. The main focus is set on acquiring the meaning of various perceptual categories (e. g. red, blue, circle, above, etc.), rather than specific world entities (e. g. an apple, a toy, etc.). Our probabilistic model is based on a varia…
Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques
2022
In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has …
Fake News. Soluzioni design driven per il citizen journalism
2022
Il progetto PO FESR “Fake News” sviluppa una ricerca che mette in campo le metodologie e gli strumenti progettuali del design della comunicazione e dell’informazione con l’obiettivo di elaborare nuove possibilità di rendere più efficaci i meccanismi automatici di valutazione dell’autenticità, originalità e rilevanza dell’informazione prodotta dal giornalismo partecipativo. Nell’ambito interdisciplinare (giornalismo, informatica, sociologia, etc.) interessato dal progetto, il contributo del design ha articolato un’ampia ricognizione sulle molteplici dimensioni del fenomeno e in particolare sulle interazioni tra design dell’informazione e giornalismo etico e partecipativo. Le connessioni tra …
Fake News. Progetto di un algoritmo contro le false verità
2022
As part of the Smart Specialization Strategy (RIS3), funded by the European Union, the Sicilian Region has promoted the Fake News project in agreement with the aim of developing information exchange and comparison tools online. The goal is to concentrate European resources in emerging technological sectors that can really develop in the same region by focusing on the construction of local knowledge, rather than on the transfer of high-cost external technological resources. Fake News project has the main objective the experimentation of advanced ICT technologies (artificial intelligence) applied to the phenomenon of disinformation, through the dialogue between the private entrepreneur (a com…
Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis
2012
AbstractThe advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of pre…
Adversarial Machine Learning in e-Health: Attacking a Smart Prescription System
2022
Machine learning (ML) algorithms are the basis of many services we rely on in our everyday life. For this reason, a new research line has recently emerged with the aim of investigating how ML can be misled by adversarial examples. In this paper we address an e-health scenario in which an automatic system for prescriptions can be deceived by inputs forged to subvert the model's prediction. In particular, we present an algorithm capable of generating a precise sequence of moves that the adversary has to take in order to elude the automatic prescription service. Experimental analyses performed on a real dataset of patients' clinical records show that a minimal alteration of the clinical record…