Browsing by Subject "Aprenentatge automàtic"
Now showing items 21-40 of 1003
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A lightweight perception module for planning purposes
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
Conference lecture
Open AccessSensing is an essential component for robots to perform the manipulation tasks in real environments. This study proposes a lightweight deep-learning-based sensing modules which allows the robots to automatically model the ... -
A lower bound for learning distributions generated by probabilistic automata
(Springer, 2010)
Conference report
Open AccessKnown algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. ... -
A machine learning approach for layout inference in spreadsheets
(SciTePress, 2016)
Conference report
Open AccessSpreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and ... -
A machine learning approach to calculate the dropout risk factor in a children's programming school
(Universitat Politècnica de Catalunya, 2019-01-29)
Bachelor thesis
Restricted access - confidentiality agreement -
A machine learning approach to stock screening with fundamental analysis
(Universitat Politècnica de Catalunya, 2019-04-15)
Master thesis
Open AccessWe present HPC.FASSR, a High-Performance Computation Fundamental Analysis Stock Screener and Ranker to compare many ML models and the criteria of famous Benjamin Graham for stock investing using fundamental data. FASSR is ... -
A machine learning approach to the identification of encrypted web traffic
(Universitat Politècnica de Catalunya, 2017-06-29)
Bachelor thesis
Restricted access - confidentiality agreement -
A machine learning enabled network planning tool
(Institute of Electrical and Electronics Engineers (IEEE), 2016)
Conference report
Restricted access - publisher's policyIn the coming years, planning future mobile networks will be infinitely more complex than nowadays. Future networks are expected to present multiple Network Management (NM) challenges to operators, such as managing network ... -
A machine learning methodology for structural damage classification in structural health monitoring
(2017)
Conference report
Restricted access - publisher's policyOne of the goals of structural health monitoring (SHM) applications is to determine the presence and the severity of a damage. In some cases, this is an element to forecast the behaviour and take decisions to allocate ... -
A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases
(I6doc.com, 2016)
Conference report
Open AccessMachine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to ... -
A machine learning workflow for hurricane prediction
(Barcelona Supercomputing Center, 2018-04-24)
Conference report
Open Access -
A methodological approach for algorithmic composition systems' parameter spaces aesthetic exploration
(Institute of Electrical and Electronics Engineers (IEEE), 2017)
Conference lecture
Open AccessAlgorithmic composition is the process of creating musical material by means of formal methods. As a consequence of its design, algorithmic composition systems are (explicitly or implicitly) described in terms of parameters. ... -
A mobile network planning tool based on data analytics
(HINDAWI, 2017-02-05)
Article
Open AccessPlanning future mobile networks entails multiple challenges due to the high complexity of the network to be managed. Beyond 4G and 5G networks are expected to be characterized by a high densification of nodes and heterogeneity ... -
A multi-scale smoothing kernel for measuring time-series similarity
(2015-11-01)
Article
Open AccessIn this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly ... -
A multiscale method for periodic structures using domain decomposition and ECM-hyperreduction
(2020-08-15)
Article
Restricted access - publisher's policyThis paper presents a nonlinear multiscale approach for periodic structures in the quasi-static, small strain regime. The approach consists in combining a domain decomposition method in which interface conditions are ... -
A new kernelized associative memory and some of its applications
(IOS Press, 2016)
Conference report
Open AccessThe classical Bidirectional Associative Memory (BAM) allows for the storage of pairs of vectors, such that when either member of the pair is presented to the BAM, the other member may be successfully recalled. This work ... -
A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning
(Institute of Electrical and Electronics Engineers (IEEE), 2017)
Conference lecture
Open AccessThe proposed paper introduces a novel method for range estimation of acoustic sources, both cetaceans and industrial sources, in deep sea environments using supervised learning with neural networks in the contex of a single ... -
A novel methodology to predict regulations using deep learning
(Single European Sky ATM Research (SESAR), 2020)
Conference report
Open AccessThe current air traffic control system tries to allocate as many flights as possible in a scenario that is expected to be time-efficient, cost-efficient, and safe. To guaranty these safety conditions, it is performed a ... -
A probabilistic approach to the visual exploration of G protein-coupled receptor sequences
(2011)
Conference report
Open AccessThe study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and ... -
A probabilistic tri-class Support Vector Machine
(2010-07)
Article
Open AccessA probabilistic interpretation for the output obtained from a tri-class Support Vector Machine into a multi-classification problem is presented in this paper. Probabilistic outputs are defined when solving a multi-class ... -
A proportional controller based on clustering theory: an academic example of a machine learning discipline
(EDP Sciences, 2018)
Conference report
Open AccessThe main objective of this paper is to present a controller design based on the K-means clustering theory. The controller is realized in such way that when the plant output is located outside of the designed clustering ...
