Sökning: "Multiple Layer Perceptron"

Visar resultat 1 - 5 av 9 uppsatser innehållade orden Multiple Layer Perceptron.

  1. 1. Attention-based Multi-Behavior Sequential Network for E-commerce Recommendation

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Zilong Li; [2022]
    Nyckelord :Recommendation System; Sequential Recommendation; Click-through Rate Model; Transformer; Multi-Task Learning; Sistema di Raccomandazione; Raccomandazione Sequenziale; Modello di Percentuale di Clic; Trasformatore; Apprendimento Multitasking; Rekommendationssystem; Sekventiell rekommendation; Klickfrekvensmodell; Transformator; Multi-Task Learning;

    Sammanfattning : The original intention of the recommender system is to solve the problem of information explosion, hoping to help users find the content they need more efficiently. In an e-commerce platform, users typically interact with items that they are interested in or need in a variety of ways. For example, buying, browsing details, etc. LÄS MER

  3. 2. Survivability Prediction and Analysis using Interpretable Machine Learning : A Study on Protecting Ships in Naval Electronic Warfare

    Master-uppsats, Linköpings universitet/Statistik och maskininlärning

    Författare :Sidney Rydström; [2022]
    Nyckelord :Electronic warfare; machine learning; statistics; Artificial Neural Networks; ANN; multi-layer perceptron; multi-task learning; interpretable machine learning; Shapley values; kernel SHAP;

    Sammanfattning : Computer simulation is a commonly applied technique for studying electronic warfare duels. This thesis aims to apply machine learning techniques to convert simulation output data into knowledge and insights regarding defensive actions for a ship facing multiple hostile missiles. LÄS MER

  4. 3. Learning to Price Apartments in Swedish Cities

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Fredrik Segerhammar; [2021]
    Nyckelord :Real estate; property; machine learning; area data; artificial neural network; random forest; home valuation; price prediction; Fastigheter; maskininlärning; områdesdata; neurala nätverk; slumpmässig skog; hemvärdering; prisförutsägelse;

    Sammanfattning : This thesis tackles the problem of accurately pricing apartments in large Swedish cities using geospatial data. The aim is to determine if geospatial data and population statistics can be used in conjunction with direct apartment data to accurately price apartments in large cities. LÄS MER

  5. 4. Skip connection in a MLP network for Parkinson’s classification

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Tim Steinholtz; [2021]
    Nyckelord :Parkinson’s Disease Classifications; Vocal Features; Multilayer Perceptron; DenseNet; Skip Connections; Highway Connections; Neural networks; Multiple Sound Types; Klassificering för Parkinsons sjukdom; MultiLayer Perceptron; Genvägs kopplingar; Röst attribut; DenseNet; Neurala Nätverk; Flertal Ljudkällor;

    Sammanfattning : In this thesis, two different architecture designs of a Multi-Layer Perceptron network have been implemented. One architecture being an ordinary MLP, and in the other adding DenseNet inspired skip connections to an MLP architecture. LÄS MER

  7. 5. A comparative study of Neural Network Forecasting models on the M4 competition data

    Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

    Författare :Markus Ridhagen; Petter Lind; [2021]
    Nyckelord :Time series analysis; M4; neural network; NN; artificial neural network; ANN; feedforward neural network; FNN; multilayer perceptron; MLP; recurrent neural network; RNN; long short-term memory; LSTM;

    Sammanfattning : The development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. LÄS MER