Blar i NTNU Open på forfatter "Langseth, Helge"

Viser treff 61-80 av 112

    • Learning robot soccer with UCT 

      Holen, Vidar; Marøy, Audun (Master thesis, 2008)
      Upper Confidence bounds applied to Trees, or UCT, has shown promise for reinforcement learning problems in different kinds of games, but most of the work has been on turn based games and single agent scenarios. In this ...

    • Learning similarity measures from data 

      Mathisen, Bjørn Magnus; Aamodt, Agnar; Langseth, Helge; Bach, Kerstin (Journal article; Peer reviewed, 2019)
      Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar ...

    • Learning to play Starcraft with Case-based Reasoning: Investigating issues in large-scale case-based planning 

      Eriksson, Jan; Tornes, Dag Øyvind (Master thesis, 2012)
      In this master thesis we describe our work in creating a planner for the real-time strategy game Starcraft using case-based reasoning. Our work has been focused on the challenges in creating a usable casebase, and the ...

    • Machine Learning in Financial Market Surveillance: A Survey 

      Tiwari, Shweta; Ramampiaro, Heri; Langseth, Helge (Peer reviewed; Journal article, 2021)
      The use of machine learning for anomaly detection is a well-studied topic within various application domains. However, the detection problem for market surveillance remains challenging due to the lack of labeled data and ...

    • MAP inference in dynamic hybrid Bayesian networks 

      Ramos-López, Dario; Masegosa, Andres; Martinez, Ana M.; Salmeron, Antonio; Nielsen, Thomas D.; Langseth, Helge; Madsen, Anders L. (Journal article; Peer reviewed, 2017)
      In this paper, we study the maximum a posteriori (MAP) problem in dynamic hybrid Bayesian networks. We are interested in finding the sequence of values of a class variable that maximizes the posterior probability given ...

    • Modeling concept drift: A probabilistic graphical model based approach 

      Borchani, Hanen; Martinez, Ana M.; Masegosa, Andres; Langseth, Helge; Nielsen, Thomas D.; Salmeron, Antonio; Fernandez, Antonio; Madsen, Anders L.; Sáez, Ramón (Journal article; Peer reviewed, 2015-11-22)
      An often used approach for detecting and adapting to concept drift when doing classification is to treat the data as i.i.d. and use changes in classification accuracy as an indication of concept drift. In this paper, ...

    • Modular Actor-Critic System for Automated Security Trading 

      Aas, Sebastian; Trygstad, Mattis Levik (Master thesis, 2022)
      Moderne finansielle markeder er i stor grad preget av algoritme-basert handel av verdipapirer. På tross av flere fordeler, er algoritme-basert handel avhengig av god domenekunnskap innen finans. Selv om markedseffisiens ...

    • Modular Actor-Critic System for Automated Security Trading 

      Trygstad, Mattis Levik; Aas, Sebastian (Master thesis, 2022)
      Moderne finansielle markeder er i stor grad preget av algoritme-basert handel av verdipapirer. På tross av flere fordeler, er algoritme-basert handel avhengig av god domenekunnskap innen finans. Selv om markedseffisiens ...

    • Motion Classification with Neural Ordinary Differential Equations 

      Johannessen, Albert (Master thesis, 2022)
      Tidsserie-klassifikasjon er et bredt fagfelt med mange forskjellige metoder. Bevegelses-klassifisering er en underkategori av tidsserie-klassifikasjon hvor tidsseriene er hentet fra mekaniske systemer. Noen eksempler på ...

    • Multiple Instance Learning for Car Brand Classification in the Leboncoin Online Marketplace 

      Hallen, Martin (Master thesis, 2017)
      This research project applies machine learning to a large-scale dataset of car images from the marketplace website Leboncoin. The project develops a model that classifies images from sales ads of cars with the brand of the ...

    • Nurse Scheduling and Rescheduling: Combining Optimization with Machine Learning-Driven Demand Predictions 

      Johansen, Anne-Sofie; Nag, Bendik; Tveit, Herborg Hermansen (Master thesis, 2023)
      Turnusplanlegging er avgjørende for alle sykehus for å sikre tilstrekkelig pasientbehandling og balansert arbeidsbelastning for sykepleierne. Det er et komplekst problem, og turnusplanen er utsatt for usikkerhet både i ...

    • Nurse Scheduling and Rescheduling: Combining Optimization with Machine Learning-Driven Demand Predictions 

      Johansen, Anne-Sofie; Nag, Bendik; Tveit, Herborg Hermansen (Master thesis, 2023)
      Turnusplanlegging er avgjørende for alle sykehus for å sikre tilstrekkelig pasientbehandling og balansert arbeidsbelastning for sykepleierne. Det er et komplekst problem, og turnusplanen er utsatt for usikkerhet både i ...

    • Odds fluctuations and potential trading profit for popular betting events 

      Bergfjord, Magne Kristian (Master thesis, 2007)
      Traditionally, to make money on sports betting, one should have a high knowledge about the teams involved, and some luck. With the introduction of online betting exchanges, one can take the role of a bookmaker and offer ...

    • On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness 

      Gundersen, Odd Erik; Shamsaliei, Saeid; Kjærnli, Håkon Slåtten; Langseth, Helge (Chapter, 2023)
      The performance of neural networks differ when the only difference is the seed initializing the pseudo-random number generator that generates random numbers for their training. In this paper we are concerned with how random ...

    • Online Failure Detection 

      Bakken, Jon Erik (Master thesis, 2007)
      Well drilling is an expensive process. The cost of keeping people and equipment on the drill site cost several hundred thousand to millions each day. Some fault situations halt the operation and demand time consuming ...

    • Opponent Modeling and Strategic Reasoning in the Real-time Strategy Game Starcraft 

      Fjell, Magnus Sellereite; Møllersen, Stian Veum (Master thesis, 2012)
      Since the release of BWAPI in 2009, StarCraft has taken the position as the leading platform for research in artificial intelligence in real-time strategy games. With competitions being held annually at AIIDE and CIG, there ...

    • Power Wave Analysis and Prediction of Faults in the Norwegian Power Grid 

      Meen, Halvor Kvernes; Jahr, Camilla (Master thesis, 2020)
      Det moderne samfunnet har blitt avhengig av elektrisitet, og som følger av dette har strømnettet blitt en viktig del av infrastrukturen vår. Å tilby et stabilt kraftdistribusjonsnett er ekstremt viktig og sørger for at ...

    • Power Wave Analysis and Prediction of Faults in the Norwegian Power Grid 

      Meen, Halvor Kvernes; Jahr, Camilla (Master thesis, 2020)
      Det moderne samfunnet har blitt avhengig av elektrisitet, og som følger av dette har strømnettet blitt en viktig del av infrastrukturen vår. Å tilby et stabilt kraftdistribusjonsnett er ekstremt viktig og sørger for at ...

    • Predicting E-commerce Consumer Behaviour Using Sparse Session Data 

      Thorrud, Thorstein Kaldahl; Myklatun, Øyvind (Master thesis, 2015)
      This thesis research consumer behavior in an e-commerce domain by using a data set of sparse session data collected from an anonymous European e-commerce site. The goal is to predict whether a consumer session results in ...

    • Predicting faults in power grids using machine learning methods 

      Santi, Vemund (Master thesis, 2019)
      I en verden som er stadig mer avhengig av elektrisitet, er det av svært viktig å sørge for et stabilt kraftdistributionsnett. Med de siste fremskrittene innen smart grid teknologi, er aktører som tidligere pleide å være ...