Sökning: "Classical Machine Learning Algorithm"

Visar resultat 1 - 5 av 18 uppsatser innehållade orden Classical Machine Learning Algorithm.

1. 1. Segmentation of Neuronal Cells Using Simplistic Methods : A Comparison of the Mean Shift Algorithm and Otsu’s Method

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

   Författare :Alex Gunnarsson; Filip Karlsson; [2023]
   Nyckelord :;

   Sammanfattning : Information regarding specific neuronal characteristics, such as shape and distribution, is essential for quantifying the brain structure and modelling accurate computer simulations. To this end, it is important to perform cell segmentation; to isolate the cells in a given image from the surrounding tissue, so it can be further analysed. LÄS MER

3. 2. Quantum Reinforcement Learning for Sensor-Assisted Robot Navigation Tasks

   Master-uppsats, Lunds universitet/Fysiska institutionen

   Författare :Joyce Cobussen; [2023]
   Nyckelord :Physics and Astronomy;

   Sammanfattning : Quantum computing has advanced rapidly throughout the past decade, both from a hardware and software point of view. A variety of algorithms have been developed that are suitable for the current generation of quantum devices, which are referred to as noisy intermediate-scale quantum devices. LÄS MER

4. 3. Imitation Learning on Branching Strategies for Branch and Bound Problems

   Master-uppsats, KTH/Matematisk statistik

   Författare :Magnus Axén; [2023]
   Nyckelord :Graph Networks; Convolutions; MIP; Branch and Bound; Facility Location Problem; MDP; Imitation Learning; Graf nätverk; Faltning; Blandade heltaltsproblem; Branch and Bound; Facility Location Problem; Markov; Imitationsinlärning;

   Sammanfattning : A new branch of machine and deep learning models has evolved in constrained optimization, specifically in mixed integer programming problems (MIP). These models draw inspiration from earlier solver methods, primarily the heuristic, branch and bound. LÄS MER

5. 4. On the Modelling of Stochastic Gradient Descent with Stochastic Differential Equations

   Master-uppsats, Uppsala universitet/Analys och partiella differentialekvationer

   Författare :Martin Leino; [2023]
   Nyckelord :stochastic gradient descent; stochastic differential equations; statistical machine learning;

   Sammanfattning : Stochastic gradient descent (SGD) is arguably the most important algorithm used in optimization problems for large-scale machine learning. Its behaviour has been studied extensively from the viewpoint of mathematical analysis and probability theory; it is widely held that in the limit where the learning rate in the algorithm tends to zero, a specific stochastic differential equation becomes an adequate model of the dynamics of the algorithm. LÄS MER

7. 5. Predict Saving Behavior - Artificial Neural Network & Machine Learning

   Master-uppsats, Lunds universitet/Nationalekonomiska institutionen

   Författare :William Möllestam; [2022]
   Nyckelord :Saving Behavior; Artificial Neural Network; Machine Learning; XGBoost; SVM; Business and Economics;

   Sammanfattning : This study aims to predict saving behavior using Artificial Neural Network (ANN), XGBoost, and Support Vector Machine (SVM) algorithms. First, 25 variables were chosen from the original 217 questions asked by the National Financial Capability Well-Being Survey (2018) NFCS, using exploratory data analysis. LÄS MER