Browsing ODA Open Digital Archive by Author "Goodwin, Morten"

Now showing items 1-11 of 11

    • A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme 

      Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, John; Goodwin, Morten (Journal article; Peer reviewed, 2014)
      Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should ...

    • Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow 

      Yazidi, Anis; Abolpour Mofrad, Asieh; Goodwin, Morten; Hammer, Hugo Lewi; Arntzen, Erik (Cognitive Neurodynamics;14, Journal article; Peer reviewed, 2020-08-27)
      An adaptive task difficulty assignment method which we reckon as balanced difficulty task finder (BDTF) is proposed in this paper. The aim is to recommend tasks to a learner using a trade-off between skills of the learner and ...

    • Deep Learning for Classifying Physical Activities from Accelerometer Data 

      Nunavath, Vimala; Johansen, Sahand; Johannessen, Tommy Sandtorv; Jiao, Lei; Hansen, Bjørge Hermann; Stølevik, Sveinung Berntsen; Goodwin, Morten (Sensors;Volume 21 / Issue 16, Peer reviewed; Journal article, 2021-08-18)
      Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening ...

    • Distributed Learning Automata-based S-learning scheme for classification 

      Goodwin, Morten; Yazidi, Anis; Jonassen, Tore Møller (Pattern Analysis and Applications;Published online 12 October 2019, Journal article; Peer reviewed, 2019-09-10)
      This paper proposes a novel classifier based on the theory of Learning Automata (LA), reckoned to as PolyLA. The essence of our scheme is to search for a separator in the feature space by imposing an LA-based random walk ...

    • Distributed learning automata-based scheme for classification using novel pursuit scheme 

      Goodwin, Morten; Yazidi, Anis (Applied intelligenc;volume 50, issue 7, Peer reviewed; Journal article, 2020-03-02)
      Learning Automata (LA) is a popular decision making mechanism to “determine the optimal action out of a set of allowable actions” (Agache and Oommen, IEEE Trans Syst Man Cybern-Part B Cybern 2002(6): 738–749, 2002). The ...

    • Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise 

      Meng, Li; Goodwin, Morten; Yazidi, Anis; Engelstad, Paal (Peer reviewed; Journal article, 2022)
      Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst them. It ...

    • On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth 

      Yazidi, Anis; Oommen, John; Goodwin, Morten (Peer reviewed; Chapter, 2015)
      In many applications, data from different sensors are aggregated in order to obtain more reliable information about the process that the sensors are monitoring. However, the quality of the aggregated ...

    • PolyACO+: a multi-level polygon-based ant colony optimisation classifier 

      Goodwin, Morten; Tufteland, Torry; Ødesneltvedt, Guro; Yazidi, Anis (Swarm Intelligence;Volume 11, Issue 3–4, Journal article; Peer reviewed, 2017-12)
      Ant Colony Optimisation for classification has mostly been limited to rule based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt ...

    • Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook 

      Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen; Moyano, Marta; Oomen, Rebekah Alice; Rasmussen, Jeppe Have; Sørdalen, Tonje Knutsen; Thorbjørnsen, Susanna Huneide (ICES Journal of Marine Science;, Peer reviewed; Journal article, 2022-01-14)
      The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from ...

    • Unsupervised State Representation Learning in Partially Observable Atari Games 

      Meng, Li; Goodwin, Morten; Yazidi, Anis; Engelstad, Paal E. (Lecture Notes in Computer Science;, Chapter; Peer reviewed; Conference object; Journal article, 2023)
      State representation learning aims to capture latent factors of an environment. Although some researchers realize the connections between masked image modeling and contrastive representation learning, the effort is focused ...

    • Using Tsetlin Machine to discover interpretable rules in natural language processing applications 

      Saha, Rupsa; Granmo, Ole-Christoffer; Goodwin, Morten (Expert systems;, Peer reviewed; Journal article, 2021)
      Tsetlin Machines (TM) use finite state machines for learning and propositional logic to represent patterns. The resulting pattern recognition approach captures information in the form of conjunctive clauses, thus facilitating ...