Sökning: "bagging"

Visar resultat 6 - 10 av 35 uppsatser innehållade ordet bagging.

  1. 6. Deep Learning Methods for Recovering Trading Strategies

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

    Författare :Erik Emtell; Oliver Spjuth; [2022]
    Nyckelord :Deep Learning; Recurrent Neural Network; Convolutional Neural Network; WaveNet; Ensemble Methods; Stacking; Bagging; Transfer Learning; Algorithmic Trading;

    Sammanfattning : The aim of this paper is first of all to determine whether deep learning methods can recover trading strategies based on historical price and volume data, with scarcity of real data in mind. The second aim is to evaluate the methods to generate a deep learning blueprint for strategy extraction. LÄS MER

  3. 7. Classification of Error Messages in Semi-Structured Log Files Using Machine Learning Algorithms

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Salman Khan; [2022]
    Nyckelord :;

    Sammanfattning : In Ericsson, newly developed applications are tested in an in-house testing framework. When an error occurs in the testing phase, manual inspection of errors in the log files and manual classification of these errors into production (PROD) or environment (ENV) classes cost a lot of time. LÄS MER

  4. 8. C-section birth data classification using ensemble modelling techniques and their performance analysis

    Magister-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi

    Författare :Maria Yousaf; [2022]
    Nyckelord :;

    Sammanfattning : Data mining and machine learning techniques have a wide range of applications in businesses, healthcare, organizations, and academia, to name a few. Machinelearning has been used by several academics to construct decision support systems, analyse major clinical features, extract useful information from trends in historical data, generate predictions, and classify diseases. LÄS MER

  5. 9. Ensemble Learning Applied to Classification of Malignant and Benign Breast Cancer

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

    Författare :Pierre Segerström; Felix Boltshauser; [2021]
    Nyckelord :;

    Sammanfattning : In this study, we show how ensemble learning can be useful for the future of breast cancer diagnosis. The chosen ensemble learning method was bagging, which made use of the classifiers Support Vector Machine (SVM), Decision Tree (DT) and Naive Bayes (NB) in order to classify mammograms as benign or malignant. LÄS MER

  7. 10. Radar based tank level measurement using machine learning : Agricultural machines

    Master-uppsats, Linköpings universitet/Programvara och system

    Författare :Daniel Thorén; [2021]
    Nyckelord :Machine Learning; Radar; AI; Tank level measurement; Linear Regression; Support Vector Regression; Bagging; Bagged Trees; Bagged Regression Trees; Boosting; Boosted Trees; Boosted Regression Trees; Random Forest; Multi Layer Perceptron Regressor; Neural Networks; Regression; Maskininlärning; Radar; Nivåmätning; Regression;

    Sammanfattning : Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. LÄS MER